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Knowledge Knowledge Integration by Integration by
Genetic Algorithms Genetic Algorithms
Knowledge Knowledge Integration by Integration by
Genetic Algorithms Genetic Algorithms
Prof. Tzung-Pei HongProf. Tzung-Pei HongDepartment of Electrical Engineering NatDepartment of Electrical Engineering Nat
ional University of kaohsiungional University of kaohsiung
T. P. T. P. HongHong
22
Outline
Introduction Review
GAs Fuzzy Sets Related Studies
Knowledge Integration StrategiesClassification RulesAssociation Rules
Conclusions
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Why Knowledge Integration
Four Reasons
Expert System
4. Reduce the effort on developing an expert system or decision support system
… …
1. Knowledge is distributed among sources
RB1 RBi RBn
GRB
User Interface
Integration
3. Knowledge can be reused
2. It Increases reliability of knowledge-based systems
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Why Using GAs ?
Integration
… …RB1 RBi RBn
Integration must satisfy
1.Completeness 2.Correctness 3.Consistency 4.Conciseness
Multi-objective optimization problem
GAs finding optimal or nearly optimal solutions
T. P. T. P. HongHong
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Vague Knowledge
In Real-World Applications
… …RB1 RBi RBn
knowledge sources or data linguistic or ambiguous information
Vagueness greatly influences the resulting knowledge base
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Benefits
Medsker [95]Knowledge integrated from different sources has good validityIntegrated knowledge can deal with more complex problemsKnowledge integration may improve the performance of the knowledge baseIntegrating would facilitate building bigger and better systems cheaply
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Traditional Knowledge Integration
ProblemsWhen conflict occurs
Domain experts must intervene in the integration process
SubjectiveTime consumingLimited Integration
A small number of knowledge sources
more knowledge sourcesMore difficult and complex
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Our Goals
Solve potential conflicts and contradictions
Integrate knowledge without human expert’s intervention
Improve the integration speed
Make the scale of knowledge sources
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History of GAs
GA: Genetic AlgorithmHistory
John Holland 1975
K. A. De Jong D. E. Goldberg
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Idea of GA
Survival of the fittest Iterative Procedure Genetic operators
ReproductionCrossoverMutation
Near optimal solution
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Simple Genetic Algorithms
Quit if : 1) Maximum generations are reached 2) Time limit is reached 3) Population is converged
Start
Initialize apopulation of individuals
Evaluate eachindividual's fitness value
Select the superior individuals for reproduction
Apply crossover and perhaps mutation
Evaluate new individual's fitness value
Quit ?YesNo
stop
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An Example
A Function
Find the max
)t5(6sin)1.0t)(2(ln125.3e)t(f2
]1,0[t
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Step1
Define a suitable representation Each Chromosome
12 bits e.g.
t = 0 000000000000 t = 1 111111111111 t = 0.680 101011100001
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Step2
Create an initial population of N N Population size Assume N = 40
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Step3
Define a suitable fitness function f to evaluate the individuals
Fitness function f(t)e.g. The first six individuals
No. bit string t f(t)1 0001100000001 0,094 0.974
2 010011001101 0.300 0.917
3 000111111100 0.124 0.644
4 101101000111 0.705 0.444
5 111011000100 0.923 0.154
6 011100111111 0.453 0.125
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Step 4
Perform the crossover and the mutation operations to generate the possible offsprings
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Crossover
Offsprings:Inheriting some characteristics of their parents
e.g. Parent 1 : 00011 0000001Parent 2 : 01001 1001101
Child 1 : 000111001101Child 2 : 010010000001
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Mutation
Offspringspossessing different characteristics from their ascendents Preserving a reasonable level of population diversity
e.g. Bit change
e.g. Inversion1 1 1 1 0 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 1 0 0
1 1 1 1 0 1 0 0 0 1 0 0 1 1 1 0 1 1 0 0 0 1 0 0
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New Offsprings
The new offsprings produced by the operators
No. bit string t f(t)1 000110011110 0,101 0.999
2 000110000001 0.094 0.974
3 010011001101 0.300 0.917
4 000111111100 0.124 0.644
5 101101000111 0.705 0.444
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Step 5
Replace the individuale.g. The first six individuals
No. bit string t f(t)1 000110011110 0,101 0.999
2 000110000001 0.094 0.974
3 010011001101 0.300 0.917
4 000111111100 0.124 0.644
5 101101000111 0.705 0.444
6 011100111111 0.453 0.125
NEW
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Step 6
If the termination criteria are not satisfied, go to Step 4; otherwise, stop the genetic algorithm
The termination criteria
The maximum number of generations
The time limit
The population converged
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Experiment
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Fuzzy Sets傳統電腦決策
不是對 (1) 就是錯 (0)例如: 25 歲以上是青年,那 26歲就是中年 ?
60 分以上是及格,那 60分以下就是不及格
何謂模糊在對 (1) 與錯 (0) 之間,再多加幾個等級
幾乎對 (0.8)可能對 (0.6)可能錯 (0.4)幾乎錯 (0.2)
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Fuzzy Sets
Question:168 公分到底算不算高 ?
身高 (Cm)
中矮 高
170 180160
隸屬度
再多分成幾級 連續
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Example:“Close to 0”e.g.
μA(3) = 0.01μA(1) = 0.09μA(0.25) = 0.62μA(0) = 1
Define a Membership Function:
μA(x) = 2x101
1
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Example:“Close to 0”
Very Close to 0:
μA(x) = 22
)x101
1(
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Fuzzy Set (Cont.)
Membership function [0, 1]
e.g.sunny : x → [0, 1]
0.6 sunny
0.8 sunny
0.1 sunnyx
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Fuzzy Set
SimpleIntuitively pleasingA generalization of crisp set
Vague member → non-member
Sunny Not sunny
1 0.8 0.6 0.4 0.2 0
0 or 1Non-member member
gradual
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Fuzzy Operations
交集 (AND)取較小的可能性EX: 學生聰明 (0.8) 而且 用功 (0.6) 則是模範生(0.6)
聯集 (OR)取較大的可能性EX: 學生聰明 (0.8) 或者 用功 (0.6) 則是模範生(0.8)
反面 (NOT)取與 1的差EX: 學生聰明是 0.8, 則學生不聰明 0.2
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Fuzzy Inference Example
洪老師找小老婆的條件( 大眼睛而且小嘴巴 )或者是身材好
Question : 誰是最佳女主角
大眼睛 小嘴巴 身材好陶晶瑩 0 0.8 0.3張惠妹 1 0.6 0.8李 玟 0 0.3 0.9李心潔 0.7 0.1 0.5蔡依林 0.8 0.5 0.3
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Answer對陶晶瑩 = (0 AND 0.8) OR 0.3 = 0 OR 0.3 = 0.3對張惠妹 = (1 AND 0.6) OR 0.8 = 0.8對李 玟 = (0 AND 0.3) OR 0.9 = 0.9對李心潔 = (0.7 AND 0.1) OR 0.5 = 0.5對蔡依林 = (0.8 AND 0.5) OR 0.3 = 0.5
李 玟 為最佳選擇 ! 謝謝 !
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Fuzzy Decision
A = {A1, A2, A3, A4, A5}A set of alternatives
C = {C1, C2, C3}A set of criteria
C1 (big eyes)
C2 (small mouth)
C3 (good shape)
A1 (Mary) 0 0.8 0.3
A2 (Judy) 1 0.6 0.8
A3 (Jan) 0 0.3 0.9
A4 (Mandy)
0.7 0.1 0.5
A5 (Nancy) 0.8 0.5 0.3
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Example (Cont.)
Assume : C1 and C2 or C3E (Ai) : evaluation function
E (A1) = (0 0.8) 0.3 = 0 0.3 = 0.3E (A2) = (1 0.6) 0.8 = 0.6 0.8 = 0.8E (A3) = (0 0.3) 0.9 = 0 0.9 = 0.9 the best choiceE (A4) = (0.7 0.1) 0.5 = 0.1 0.5 = 0.5E (A5) = (0.8 0.5) 0.3 = 0.5 0.3 = 0.5
C1 (big eyes) C2 (small mouth)
C3 (good shape)
A1 (Mary) 0 0.8 0.3
A2 (Judy) 1 0.6 0.8
A3 (Jan) 0 0.3 0.9
A4 (Mandy)
0.7 0.1 0.5
A5 (Nancy) 0.8 0.5 0.3
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Review of Knowledge Integration
KnowledgeIntegration
Cooperative Approach
Centralized Approach
BlackboardBlackboard LPC ModelRepertory Grid
IntegrityConstraints
Decision Table
Genetic Algorithm
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GA-Based Classifier Systems
GA-BasedClassifier Systems
Michigan Approach
Pittsburgh Approach
rule 1 xxxxxxx....rule 2 yyyyyyy....
rule n nnnnnn....
rule set 1rule set 2
rule set m
rrrrrrrrr....zzzzzzzzzzzz....
mmmm.......
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Genetic Knowledge Integration
TPGKIApproach
TPGFKIApproach
GKIDSOApproach
GFKIGMApproach
GFKILMApproach
MGKIApproach
MGFKIApproach
PittsburghApproach
Michigan Approach
Vague Knowledge
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Integration of Classification Rules
Four Methods GKIDSO
Genetic Knowledge-Integration approach with Domain-Specific Operators
TPGKITwo-Phase Genetic Knowledge Integration
GFKILMGenetic-Fuzzy Knowledge-Integration with several sets of Local Membership functions
GFKIGMGenetic-Fuzzy Knowledge-Integration with a set of Global Membership functions
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Genetic Knowledge-Integration Framework
Intermediary representation
GA-BasedKnowledge Integration
Global Feature Set &Class Set
Encoding
Integrating
M.LMethod 1
K.A. Tool 1
Expert Group 1
Training Data Set 1 Expert
Group n
Training Data Set m
K.A. Tool n
M.LMethod m
Case Set
Dictionary DictionaryDictionary DictionaryRule Set Rule Set Rule Set Rule Set
Dictionary
Intermediary representation
Intermediary representation
Intermediary representation
Knowledge Base
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Knowledge Integration
Rule Set Knowledge Input
Knowledge Encoding
GeneticKnowledge Integration
KnowledgeIntegration
Knowledge DecodingData Set
Knowledge Verification
Knowledge Base
Rule Set Rule Set
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GKIDO Approach
Genetic Knowledge-Integration approach with Domain-Specific Operators Consists of two parts
EncodingIntegration
Knowledge encoding Knowledge integration
RS
RS
RS
RS
Initial population Generation 0 Generation k
1
2
3
m
Chromosome
Chromosome
Chromosome
Chromosome
1
2
3
m
Chromosome
Chromosome
Chromosome
Chromosome
1
2
3
m
Chromosome
Chromosome
Chromosome
Chromosome
1
2
3
m
genetic
operators
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Knowledge Encoding
Rule Set
Intermediary Rule Intermediary Rule
Fixed-Length Rule String
Variable-Length Rule-Set String
Fixed-Length Rule String
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Example: Brain Tumor
Two classes: {Adenoma, Meningioma}Three features:
{Location, Calcification, Edema}Feature values for Location
{brain surface, sellar, brain stem}Feature values for Calcification
{no, marginal, vascular-like, lumpy}Feature values for Edema
{no, < 2 cm, < 0.5 hemisphere}
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Intermediary Rules
Two RulesR1:IF (Location=sellar) and (Calcification=no)
then AsenomaR2:IF (Location=brain surface) and (Edema< 2cm)
then Meningioma
R1:IF(Location=sellar) and (Calcification=no) and (Edema= no , or < 2 cm , or < 0.5 hemisphere) then AsenomaR2:IF(Location=brain surface) and (Calcification= no or marginal or vascular-like or lumpy) and (Edema< 2cm) then Meningioma
dummy test
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Fixed-Length Rule String
R1:IF(Location=sellar) and (Calcification=no) and (Edema= no , or < 2 cm , or < 0.5 hemisphere) then AsenomaR2:IF(Location=brain surface) and (Calcification= no or marginal or vascular-like or lumpy) and (Edema< 2cm) then Meningioma
R1 : 010 1000 111 10
R2 : 100 1111 010 01
Location Calcification Edema Classes
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Knowledge Integration
Initial Population
Rule Set 1
Rule Set 2
Rule Set n
CrossoverMutationFusionFission
Fitness Function
Rule Set 1
Rule Set 2
Rule Set n
Generation 1
Genetic Operation
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Fitness Function
Formally
where
fitness(RS)=Accuracy(RS)
Complexity RS( )
- is a control parameter
Accuracy =the total number of measure instances correctly matched by RS
the total number of measure instances( )RS
Complexity RSNumber of rules within the integrated rule set RS
Number of rules within initial RS mii
m( )
[ ( )] /
1
-
-
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Crossover
2cp
1
11
7
1 1
2
21
7
2 2
1
2
100110110 001 01001 0101010 0010101011 00
0100110011 00 11011 1010101 1000110011 01
1001101100 01 01001
0100110011 00 11011
RS
r r r
RS
r r r
O
O
bits
i n
bits
j m
:
:
:
:
1010101 1000110011 01
0101010 0010101011 00
1cp
crossover
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Fusion
Eliminate redundancy and subsumption
RedundancyR1: if A then BR2: if A then B
SubsumptionR1: if A and C then BR2: if A then B
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Fusion (Cont.)
Eliminate redundancy
Eliminate subsumption
k
k ki kj
RS
r r r
Ok
:
:
100110110001
100110110001
1
010010101010 010010101010
010010101010Fusion
Fusion
q
q qi qj
RS
r r r
Oq
:
:
100110110001
100110110001
1
110010101010 010010101010
110010101010
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Fission
Eliminate misclassification and contradiction
Misclassificatione: (A, C)
R: if A then B
ContradictionR: if A then B or C
R1: if A then B R2: if A then C
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Fission (Cont.)
Eliminate misclassificationSelect the "closest" near-miss rule to the wrong classified test instance for specializing
Fission
Insert
k
k ki kn
k ki kn
RS
r r r
O
r r I r
k
:
:
"
1001101100 01 1001001 100 0010101011 00
1001101100 01 1001001 100 1001001 010 0010101011 00
1
1
11
01 10
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Fission (Cont.)
Eliminate contradiction
Fission
k
k ki kn
k ki ki kn
RS
r r r
O
r r r r
k
:
:
100110110001 100100110 001010101100
100110110001 100100110 100100110 001010101100
1
11 2
110
100 010
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Experiments- Breast Cancer Diagnosis
Six knowledge sources are integrated699 cases used in the experiment
524 cases for integrating175 cases for testing
9 attributes and 2 classes Benign : 458 casesMalignant : 241 cases
Each rule is encoded into a bit string of 92 bits long
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Result
0 1 6 23 39 32 56 59 80 97100
Generation CPU Time
00:00:0000:00:0100:00:0500:00:2200:00:2800:00:3100:00:5500:00:5800:01:1900:01:3600:01:40
Accuracy
0.77200.81170.88240.90680.92280.94220.94870.95330.95560.95680.9619
Fitness
0.74950.78560.86500.87190.89590.92370.93010.93460.93680.94730.9523
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Result (Cont.)
Test cases: 175
classes case no.correctly
classification misclassification unknown
Benign
Malignant
132
43
128
41
2
2
2
0
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Experiments- Breast Cancer Diagnosis
72757881848790939699
0 1 6 23 29 32 56 59 80 97 100
Generation (α=0.125)
fitn
ess
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5757
Ten knowledge sources are integrated504 actual cases used in the application
378 cases for integrating126 cases for testing
12 attributes and 6 classes
Each rule is encoded into a bit string of 105 bits long
Application- Brain Tumor Diagnosis
Pituitary Adenoma: 85
Meningioma: 119Medulloblastoma: 68
Glioblastoma: 54
Astrocytoma: 122
Protoplasmic Astrocytoma: 56
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Application - Brain Tumor Diagnosis (Cont.)
0 150 300 450 600 750 900120013501500165018002000
Generation CPU Time00:00:0000:19:0500:38:2400:57:2201:16:2801:35:3101:54:5502:32:5802:51:1903:10:3603:29:4003:49:3104:14:24
Accuracy
0.79810.81170.82640.83210.85230.86010.87030.87910.87980.88300.88770.89070.9142
Fitness
0.53300.57010.60700.61250.62300.63370.63700.66010.66730.67100.70220.73730.7590
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5959
525558616467707376
0 600 1200 1800 2400 3000
Generation (α=0.125)
fitn
ess
Application - Brain Tumor Diagnosis
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TPGKI Approach
TPGKITwo-Phase Genetic Knowledge Integration
Consisting of two phasesKnowledge integrationKnowledge refinement
Integrating multiple rule sets by pure genetic operators
Domain-specific genetic operators need not intervene in the integration
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Two Phases
Integration phase & Refinement phases
r11
r1x
rm1
rmy
r11
r1z
rm1
rmw
RS
RS
RS
RS
1
2
3
m
RS
RS
RS
RS
1
2
3
m
RS
RS
RS
RS
1
2
3
m
PhaseIntegration
Phase
Refinement
PhaseIntegration
Phase
Refinement
PhaseIntegration
Select the best
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Knowledge-Integration Phase
Initial Population
Genetic Operation
Rule Set 1
Rule Set 2
Rule Set n
CrossoverMutation
Fitness Function
Rule Set n
Generation 1
Rule Set 1
Rule Set 2
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Knowledge-Refinement Phase
Initial Population
Rule Set 1
Rule Set i
Rule Set n
CrossoverMutation
Fitness Function
Rule 1
Rule 2
Rule m
Rule 1
Rule 2
Redundancy
SubsumptionContradiction
Genetic Operation
Generation 1
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Fitness Function
Accuracy r i
U
U Ui
i i
r
r r
( )| |
| | | |
Necessity rr
ii
r RSe U
e
r e( )
( , )
( , )
( , )
, ;
, .
r e
if e is correctly classified by a rule r
otherwise
1
0
Coverage ri rU
rU
i i( ) | | | |
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6565
Evaluation Process
Let U be the object set Calculate Accuracy ir( )
Calculate Necessity ir( )
Calculate Coverage jr( )
Sort rules by Accuracy* Necessity
Fitness=Accuracy*Necssity*Coverage
jrUU=U-Remove jr
Empty STOP
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Experiments- Breast Cancer Diagnosis
0 1 4 5 8 26 34 44 45 93 95100
Generation CPU Time
00:00:0000:00:0200:00:1000:00:1300:00:2000:01:0400:01:2500:01:5000:01:5200:03:5100:03:5700:04:12
Accuracy
0.77200.81910.85810.92060.94770.94830.95250.95600.96570.96590.96740.9793
Fitness
0.74950.78750.82500.91120.91190.92470.92810.93750.94280.94690.94840.9502
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6767
72757881848790939699
0 1 4 5 8 26 34 44 45 93 95 100
Generation (α=0.125)
fitn
ess
Experiments- Breast Cancer Diagnosis
T. P. T. P. HongHong
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Application- Brain Tumor Diagnosis
0 150 300 450 600 750 900120013501500165018002000
Generation CPU Time
00:00:0000:31:0501:02:1301:33:4202:04:3302:35:3103:07:5504:09:3804:41:0305:12:3905:43:4006:25:3106:59:05
Accuracy
0.79810.81910.82960.84720.85830.87530.89030.89890.90120.90570.91070.91620.9257
Fitness
0.57440.58010.60700.72450.80150.81780.83270.85010.85230.85410.85830.86210.8700
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565962656871747780838689
0 600 1200 1800 2400 3000
Generation (α=0.125)
fitn
ess
Application - Brain Tumor Diagnosis
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Comparison of GKIDSO and TPGKI
Experiment: Breast Cancer Diagnosis
Application: Brian Tumor Diagnosis
GKIDSO
TPGKI
Approach CPU Time Accuracy Rule No.
1096.10%100
97.93% 7252(100 generations)
(100 generations)
GKIDSO
TPGKI
Approach CPU Time Accuracy
9291.42%15264
92.57% 8625145(2000 generations)
(2000 generations)
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Genetic-Fuzzy Knowledge-Integration
GFKILMGenetic-Fuzzy Knowledge-Integration with several sets of Local Membership functionsAssociated with several sets of local membership functions
GFKIGM ApproachGenetic-Fuzzy Knowledge-Integration with a set of Global Membership functionsAssociated with a set of global membership functions
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Genetic-Fuzzy Knowledge-Integration Framework
Intermediary representation
Knowledge Integration
Expert Group 1
Training Set 1 Expert Group n
M.LMethod 1
K.A. Tool 1
Training Set m
K.A. Tool n
M.LMethod m
Intermediary representation
Intermediary representation
Intermediary representation
Encoding
Integrating
Functions Fuzzy Rule Set Membership
Genetic Fuzzy
Records
FunctionsMembership
FunctionsMembershipMembership
Functions Fuzzy Rule Set
Fuzzy Rule Set
Fuzzy Rule Set
Instances
Testobjects
Fuzzy Rule Set+
Membership Functions
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GFKILM Approach
GFKILM approach consists of two partsEncodingIntegration
Knowledge encoding Knowledge integrationInitial population Generation 0 Generation k
Chromosome
Chromosome
Chromosome
Chromosome
1
2
3
m
Chromosome
Chromosome
Chromosome
Chromosome
1
2
3
m
genetic
operators
Chromosome
Chromosome
Chromosome
Chromosome
1
2
3
m
+MFS11~ ~RS
+MFS22~ ~RS
+MFS33~ ~RS
+MFSmm~ ~RS
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Knowledge Encoding
Rule Set+MFS
Intermediary Rule+MFS
Fixed-Length Rule StringAssociated with MFS
Associated with MFS
Intermediary Rule+MFS
Fixed-Length Rule StringAssociated with MFS
Variable-Length Rule-Set String
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Examples: IRIS Flowers
花萼長度
u(S.L.)
4.3 7.95.2 6.1 7.0
Short Medium Long
S.L.
花萼寬度
u(S.W. )
Medium
2.0 4.42.6 3.2 3.8
Narrow Wide
S.W.
花瓣長度 花瓣寬度
u(P.L. )
1.0 6.92.4 3.9 5.4
Short Medium Long
P.L.
u(P.W. )
Medium
01 2.50.7 1.3 1.9
Narrow Wide
P.W.
Setosa =1, Versicolor=2, Virginica=3
T. P. T. P. HongHong
7676
Examples
IF P.L.=Short Then Setosa
IF S.L.=(Short or Medium or Long) and S.W.=(Narrow or Medium or Wide)and P.L.=Short and P.W.=(Narrow or Medium or Wide) Then Setosa
Intermediary Representation
Membership functions + Fuzzy Rules
ClassWPLP
WSLS
qr
100.
6.0,9.1,6.0,3.1,6.0,7.0.
5.1,4.5,5.1,9.3,5.1,4.2
.6.0,8.3,6.0,2.3,6.0,6.2
.9.0,0.7,9.0,1.6,9.0,2.5:1
~
T. P. T. P. HongHong
7777
Knowledge Integration
Initial Population
Genetic Operation
RS1+MFS
RS2+MFS
RSn+MFS
CrossoverMutationFusion
Fitness Function
Generation 1
RS1+MFS
RS2+MFS
RSn+MFS
T. P. T. P. HongHong
7878
Crossover
1 52 0 9 61 0 9 7 0 0 9 2 6 0 6 32 0 6 38 0 6 2 4 16 39 14 54 15 0 6 0 6 12 0 6 19 0 6~~ : ... . , . , . , . , . , ..
. , . , . , . , . , ..
. , . , . , . , . , ..
. , . , . , . , . , ..
RS
S L S W P L PW
001
Class....
2 5 2 0 8 61 0 7 7 0 0 8 2 0 0 3 2 5 0 4 38 0 9 2 4 16 4 0 14 5 4 15 0 6 0 7 12 0 6 19 0 6~~ : ... . , . , . , . , . , ..
. , . , . , . , . , ..
. , . , . , . , . , ..
. , . , . , . , . , ..
RS
S L S W P L P W
010
Class....
crossover point
1 5 2 0 9 61 0 9 7 0 0 9 2 6 0 6 2 5 0 4 38 0 9 2 4 16 4 0 14 5 4 15 0 6 0 7 12 0 6 19 0 6'~ ~ : ... . , . , . , . , . , ..
. , . , . , . , . , ..
. , . , . , . , . , ..
. , . , . , . , . , ..
RS
S L S W P L P W
010
Class....
2 5 2 0 8 61 0 7 7 0 0 8 2 0 0 3 3 2 0 6 38 0 6 2 4 16 3 9 14 5 4 15 0 6 0 6 12 0 6 19 0 6'~~ : ... . , . , . , . , . , ..
. , . , . , . , . , ..
. , . , . , . , . , ..
. , . , . , . , . , ..
RS
S L S W P L P W
001
Class....
out of sequence
crossover
1 5 2 0 9 6 1 0 9 7 0 0 9 2 5 0 4 2 6 0 6 38 0 9 2 4 16 4 0 1 4 5 4 15 0 6 0 7 1 2 0 6 19 0 6'~ ~ : ... . , . , . , . , . , ..
. , . , . , . , . , ..
. , . , . , . , . , ..
. , . , . , . , . , ..
RS
S L S W P L P W
010
Class....
rearrange
2 5 2 0 8 61 0 7 7 0 0 8 2 0 0 3 3 2 0 6 38 0 6 2 4 16 3 9 14 5 4 15 0 6 0 6 12 0 6 19 0 6'~~ : ... . , . , . , . , . , ..
. , . , . , . , . , ..
. , . , . , . , . , ..
. , . , . , . , . , ..
RS
S L S W P L P W
001
Class....
1~ :O
2~ :O
1~ :O
2~ :O
T. P. T. P. HongHong
7979
Mutation
mutation point
out of sequence
mutation
rearrange
1 52 9 1 09 0 0 9 2 6 6 32 0 6 38 6 2 416 39 14 54 15 0 6 612 619 6 001~ ~ : ... . ,0. ,6. , . ,7. , .
.. ,0. , . , . , . ,0.
.. , . , . , . , . , .
.. ,0. , . ,0. , . ,0.
..RS
S L S W P L PW Class
...
1 52 9 1 09 0 0 9 2 6 6 5 0 6 38 6 2 4 16 39 14 54 15 0 6 612 619 6 001'~ ~ : ... . ,0. ,6. , . ,7. , .
.. ,0. ,2. , . , . ,0.
.. , . , . , . , . , .
.. ,0. , . ,0. , . ,0.
..RS
S L S W P L PW Class
...
1 52 9 1 09 0 0 9 2 5 0 6 6 6 38 6 2 416 39 14 54 15 0 6 612 619 6 001'~ ~ : ... . ,0. ,6. , . ,7. , .
.. , . ,2. ,0. , . ,0.
.. , . , . , . , . , .
.. ,0. , . ,0. , . ,0.
..RS
S L S W P L PW Class
...
1~ :O
1~ :O
T. P. T. P. HongHong
8080
Fusion
kir~
kjr~
: IF (P.L.=Short) Then Class is Setosa
: IF (P.L.=Short) Then Class is Setosa
k
S L S W P L P W
RS~ ~ : . , . , . , . . , . . , . , . , . , . , . . , . , . , . , . , . . , . , . , . , . , .
. . . . . . . .
51 0 8 6 0 0 87110 2 5 0 7 31 0 6 39 0 7 2 314 37 15 53 16 0 7 0 8 13 0 7 18 0 6 100
Class
ki
S L S W P L
r~
. , . , . , . , . , . . , . , . , . , . , . . , . , . , . , . , . .. . . . . .
5 2 0 9 6110 7 0 0 9 2 6 0 8 32 0 7 38 0 6 2 4 16 39 14 5 4 15 0
6 0 6 12 0 6 19 0 6 100, . , . , . , . , .
. .
~
P W Class
kjr
T. P. T. P. HongHong
8181
Fusion
k
S L S W P L P W Class
ki
S L S W
RS
r
~ ~
~
: , , , , , , , , , , , , , , , , , , , ,
, , , , , , , , , ,
. . . . . . . .
. . . .
111111111111111 0 1 0111111 100
1111111111111
, , , , , , , , , ,
: , , , , , , , , , , , , , , , , , , , ,
. . . .
. . . . . . . .
~
~ ~
11 0 1 0111111 100
111111111111111 0 1 0111111 100
P L P W Class
kj
k
S L S W P L P W Class
r
RS
ki
S L S W P L P W Class
kj
k
r
r
RS
~
~
~ ~
, , , , , , , , , , , , , , , , , , , ,
: . , . , . , . . , .
. . . . . . . .
111111111111111 0 1 0111111 100
51 0 8 6 0 0 8711
0 2 5 0 7 31 0 6 39 0 7 2 314 37 15 53 16 0 7 0 8 13 0 7 18 0 6 100S L S W P L P W Class
kir. . . . . . . .
. , . , . , . , . , . . , . , . , . , . , . . , . , . , . , . , .
~
Fusion If accuracy accuracy dropki kj kjr r r( ) ( ),~ ~ ~
T. P. T. P. HongHong
8282
Fusion (Subsumption)
: IF (P.L.=Short) Then Class is Setosa
: IF (P.L.=Short) and (P.W.=Narrow) Then Class is Setosa
kir~
kjr~
k
S L S W P L P W
RS~ ~ : . , . , . , . . , . . , . , . , . , . , . . , . , . , . , . , . . , . , . , . , . , .
. . . . . . . .
51 0 8 6 0 0 87110 2 5 0 7 31 0 6 39 0 7 2 314 37 15 53 16 0 7 0 8 13 0 7 18 0 6 100
Class
ki
S L S W P L
r~
. , . , . , . , . , . . , . , . , . , . , . . , . , . , . , . , . .. . . . . .
5 2 0 9 6110 7 0 0 9 2 6 0 8 32 0 7 38 0 6 2 4 16 39 14 5 4 15 0
6 0 6 12 0 6 19 0 6 100, . , . , . , . , .
. .
~
P W Class
kjr
T. P. T. P. HongHong
8383
Fusion(subsumption)
Fusion
k
S L S W P L P W Class
ki
S L S W
RS
r
~ ~
~
: , , , , , , , , , , , , , , , , , , , ,
, , , , , , , , , ,
. . . . . . . .
. . . .
111111111111111 0 1 0111111 100
1111111111111
, , , , , , , , , ,
: , , , , , , , , , , , , , , , , , , , ,
. . . .
. . . . . . . .
~
~ ~
11 0 1 0111 0 1 0 100
111111111111111 0 1 0111111 100
P L P W Class
kj
k
S L S W P L P W Class
r
RS
ki
S L S W P L P W Class
kj
k
r
r
RS
~
~
~ ~
, , , , , , , , , , , , , , , , , , , ,
: . , . , . , . . , .
. . . . . . . .
111111111111111 0 1 0111 0 1 0 100
51 0 8 6 0 0 8711
0 2 5 0 7 31 0 6 39 0 7 2 314 37 15 53 16 0 7 0 8 13 0 7 18 0 6 100S L S W P L P W Class
kir. . . . . . . .
. , . , . , . , . , . . , . , . , . , . , . . , . , . , . , . , .
~
T. P. T. P. HongHong
8484
Experiments- Hepatitis Diagnosis
Ten knowledge sources are integrated155 cases used in the experiment19 attributes and 2 classes
T. P. T. P. HongHong
8585
Experiments- Hepatitis Diagnosis
0 13 69 124 181 261 414140121103110355038174000
Generation CPU Time
00:00:0000:00:0400:00:1900:00:3500:00:5200:01:1500:01:5500:06:0700:09:2000:13:4200:15:3500:16:4500:17:36
Accuracy
0.76880.78440.81320.83280.84320.85250.86880.88760.89490.89650.89770.91830.9290
Fitness
0.75370.76900.79720.81640.82660.83570.85170.87010.87730.87890.88000.90020.9107
T. P. T. P. HongHong
8686
75
78
81
84
87
90
93
0 69 181 414 2110 3550 4000
Generation (α=0.125)
fitn
ess
Experiments- Hepatitis Diagnosis
T. P. T. P. HongHong
8787
Application : Sugar-Cane Breeding Prediction
Four knowledge sources are integrated699 actual cases used in the application36 attributes and 2 classes
T. P. T. P. HongHong
8888
Application : Sugar-Cane Breeding Prediction
Each rule is encoded into a string of 362 units long
0 2 9 17 29 37 76 230 290 392 498 99017342052210833415000
Generation CPU Time00:00:0000:00:0200:00:0800:00:1700:00:2900:00:3700:01:1600:03:5300:04:5300:06:3600:08:2200:16:3600:29:0600:34:2600:35:2200:55:5801:23:46
Accuracy0.56740.67800.68030.68680.68710.68770.69030.69040.69520.69540.71740.73520.73780.74140.74160.74490.7602
Fitness0.55620.66470.66690.67330.67420.67480.67660.67680.68150.68170.70330.72070.72330.72680.72700.73020.7452
T. P. T. P. HongHong
8989
5558616467707376
0 2 15 20 37 230 392 990 2052 3341
Generation (α=0.125)
fitn
ess
Application:Sugar-Cane Breeding Prediction
T. P. T. P. HongHong
9090
GFKIGM Approach
Genetic-Fuzzy Knowledge-Integration with a set of Global Membership functionsConsisting of two parts
Knowledge encodingKnowledge integration
Generating a fuzzy rule-set associated with a global collection of membership functions for all fuzzy rules
T. P. T. P. HongHong
9191
Knowledge Encoding
Rule Set+MFS
Variable-Length Rule-Set String
MFS String
+ MFS String
Fixed-Length Rule String
Intermediary Rule
T. P. T. P. HongHong
9292
Examples: IRIS Flowers
: IF P.L.=Short Then Setosaqr 1~
: IF P.L.=Long Then Virginicaqr 2~
: IF P.W.=Medium Then Versicolorqr 3~
: IF P.W.=Wide Then Virginicaqr 4~
T. P. T. P. HongHong
9393
Examples : IRIS Flowers
花萼長度
u(S.L.)
4.3 7.95.2 6.1 7.0
Short Medium Long
S.L.
花萼寬度
u(S.W. )
Medium
2.0 4.42.6 3.2 3.8
Narrow Wide
S.W.
花瓣長度 花瓣寬度
u(P.L. )
1.0 6.92.4 3.9 5.4
Short Medium Long
P.L.
u(P.W. )
Medium
01 2.50.7 1.3 1.9
Narrow Wide
P.W.
Setosa =1, Versicolor=2, Virginica=3
T. P. T. P. HongHong
9494
Examples : IRIS Flowers
Rule String
qr
S L S W P L PW Class
1 111 111 100 111 100'~ :. . . . . . . .
IF P.L.=Short Then Setosa
IF S.L.=(Short or Medium or Long) and S.W.=(Narrow or Medium or Wide)and P.L.=Short and P.W.=(Narrow or Medium or Wide) Then Setosa
Intermediary Representation
T. P. T. P. HongHong
9595
Examples : IRIS Flowers (Cont.)
: IF P.L.=Short Then Setosaqr 1~
: IF P.L.=Long Then Virginicaqr 2~
: IF P.W.=Medium Then Versicolorqr 3~
: IF P.W.=Wide Then Virginicaqr 4~
111 111 100 111 100 111 111 001 111 001 111 111 111 010 010 111 111 111 001 001
1 2 3 4
S L S W P L PW ClassS L S W P L PW ClassS L S W P L PW ClassS L S W P L PW Class
q q q qr r r r
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
' ' ' '~ ~ ~ ~
52 9 61 9 7 0 9 2 6 6 32 6 38 6 2 4 15 39 15 54 15. ,0. , . ,0. , . ,0. . ,0. , . ,0. , . ,0. . , . , . , . , . , .. . . . . .
Short Medium Long Narrow Medium Wide Short Medium Longu u u u u u u u u
S L S W P LMF MF MF
0 7 6 13 6 19 6. ,0. , . ,0. , . ,0.. .
Narrow Medium Wideu u u
P W
q
MF
MFS
T. P. T. P. HongHong
9696
Knowledge Integration
Initial Population
Genetic Operation
RS1+MFS
RS2+MFS
RSn+MFS
CrossoverMutationFusion
Fitness Function
Generation 1
RS1+MFS
RS2+MFS
RSn+MFS
T. P. T. P. HongHong
9797
Crossover
2 2
21 2 2 1 2
111001 101 010 010101 61 9 0 9 13 0 6 15 63
~ ~ :
~ ~ ~
. ,0. ,7. ,0. , . , . , . ,0.
' ' '
RS MFS
r r r MF MF
bits
j h A A
2rscp2mfcp 3 unit
2dcp
1 1
11 1 1 1 2
101001 011 001 110101 5 7 8 9 11 16 0 7 18 93
~ ~ :
~ ~ ~
. ,0. ,6. , . , . , . , . ,0.
' ' '
RS MFS
r r r MF MF
bits
i k A A
1rscp 1mfcp1dcp
1endcp
3 unit
1O : 101001 ......... 011 010 ......... 010101 5.7,0.8,6.9,1.1, 1.61.6, 0.6,1.51.5,0.6
111001...101 001 ... 110101 6.1,0.9,7.0,0.9, 1.6, 0.7,1.8,0.92O :
out of sequence
1O : 101001 ......... 011 010 ......... 010101 5.7,0.8,6.9,1.1, 1.51.5, 0.6,1.61.6,0.6
111001...101 001 ... 110101 6.1,0.9,7.0,0.9, 1.6, 0.7,1.8,0.92O :
Crossover
Rearrange
T. P. T. P. HongHong
9898
Mutation
rsmpmfmp
Mutation
Rearrange
New
New
1 1
11 1 1 1 2
101001 011 01 1101015 7 8 9 11 7 9~ ~ :
~ ~ ~
. ,0. ,6. , . , ,0. , ,0.
' ' '
RS MFS
r new r r MF MFi k A
out of sequence
A
1 1.6 1.2
1 1
11 1 1 1 2
101001 011 01 1101015 7 8 9 11 16 7 9~ ~ :
~ ~ ~
. ,0. ,6. , . , . ,0. , ,0.
' ' '
RS MFS
r r r MF MFi k A A
0 1.8
1 1
11 1 1 1 2
101001 011 01 1101015 7 8 9 11 7 9~ ~ :
~ ~ ~
. ,0. ,6. , . , ,0. , ,0.
' ' '
RS MFS
r new r r MF MFi k A A
1 1.2 1.6
T. P. T. P. HongHong
9999
Fusion
: IF (P.L.=Short) Then Class is Setosa
: IF (P.L.=Short) Then Class is Setosa
qir '~
qjr '~
111111100111100 111111100100100 5 2 0 9 61 0 9 7 0 0 9 0 7 0 6 13 0 6 19 0 6
qi qj S L P Wr r MF MF' '~ ~
. , . , . , . , . , . , . , . , . , . , . , .. . . .
111111100111100 111111100111100 5 2 0 9 61 0 9 7 0 0 9 0 7 0 6 13 0 6 19 0 6
qi qj S L P Wr r MF MF' '~ ~
. , . , . , . , . , . , . , . , . , . , . , .. . . .
T. P. T. P. HongHong
100100
Fusion (Subsumption)
: IF (P.L.=Short) Then Class is Setosa
: IF (P.L.=Short) and (P.W.=Narrow) Then Class is Setosa
qir '~
qjr '~
111111100111100 111111100100100 5 2 0 9 61 0 9 7 0 0 9 0 7 0 6 13 0 6 19 0 6
qi qj S L P Wr r MF MF' '~ ~
. , . , . , . , . , . , . , . , . , . , . , .. . . .
111111100111100 111111100100100 5 2 0 9 61 0 9 7 0 0 9 0 7 0 6 13 0 6 19 0 6
qi qj S L P Wr r MF MF' '~ ~
. , . , . , . , . , . , . , . , . , . , . , .. . . .
T. P. T. P. HongHong
101101
Experiments- Hepatitis Diagnosis
0 4 34 160 473 57010571495179122512580271030623342375638474000
Generation CPU Time
00:00:0000:00:0200:00:1000:00:4500:02:1400:02:4000:04:5100:06:4600:08:0300:10:0200:11:2700:12:0200:13:1300:14:4900:16:4400:17:0900:17:51
Accuracy
0.76880.78670.82280.84500.85420.85540.85780.86330.86560.87210.88370.88950.89100.90490.90560.90680.9161
Fitness
0.75730.77120.80660.82840.83740.83860.84090.84630.84860.85500.86630.87200.87350.88710.88780.88900.8981
T. P. T. P. HongHong
102102
75
78
81
84
87
90
93
0 34 473 1057 1791 2580 3062 3756 4000
Generation (α=0.125)
fitn
ess
Experiments- Hepatitis Diagnosis
T. P. T. P. HongHong
103103
Application : Sugar-Cane Breeding Prediction
Each knowledge source is encoded into a string of 542 units long
0 2 3 9 13 16 17 227 308 493138616372924315133005000
Generation CPU Time00:00:0000:00:0200:00:0300:00:0800:00:1200:00:1500:01:1600:03:4700:03:5300:08:1500:23:1400:27:2700:49:0400:52:5200:56:2201:24:37
Accuracy0.55060.67260.68020.68070.68640.68680.69220.69280.69440.71530.72010.72530.72670.72950.73620.7485
Fitness0.53450.65300.66030.66080.66640.66650.67200.67260.67410.69440.69910.70410.70550.70820.71470.7266
T. P. T. P. HongHong
104104
5255586164677073
0 2 9 13 17 308 1386 2924 3300
Generation (α=0.125)
fitn
ess
Application:Sugar-Cane Breeding Prediction
T. P. T. P. HongHong
105105
Comparison of GFKILM and GFKIGM
Experiment: Hepatitis Diagnosis
Application: Sugar-Cane Breeding Prediction
GFKILM
GFKIGM
Approach CPU Time Accuracy Rule No.
492.90%1056
91.61% 41071(4000 generations)
(4000 generations)
GFKILM
GFKIGM
Approach CPU Time Accuracy Rule No.
276.02%5026
74.85% 25077(5000 generations)
(5000 generations)
T. P. T. P. HongHong
106106
ROADMAP
TPGKIApproach
TPGFKIApproach
GKIDSOApproach
GFKIGMApproach
GFKILMApproach
MGKIApproach
MGFKIApproach
PittsburghApproach
Michigan Approach
Vague Knowledge
T. P. T. P. HongHong
107107
Why Data Mining?
Simon
Commodities
Supermarketif one customer buys milk
then he is likely to buy bread, so...
T. P. T. P. HongHong
108108
Mining Association Rules
Bread
Milk
IF bread is bought then milk is bought
T. P. T. P. HongHong
109109
The Role of Data Mining
Preprocess data
Useful patterns
Knowledge and strategy
T. P. T. P. HongHong
110110
Mining steps
Step1:Define minsup and minconfex: minsup=50%
minconf=50%
Step2:Find large itemsets
Step3:Generate association rules
T. P. T. P. HongHong
111111
ExampleLarge itemsets
TID I tems100 A C D200 B C E300 A B C E400 B E
Database
C 2
I temset{A B}{A C}{A E}{B C}{B E}{C E}
C 3
I temset{B C E}
ScanDatabas
e
ScanDatabas
e
ScanDatabas
e
I temset Sup.{A} 2{B} 3{C} 3{D} 1{E} 3
C 1
I temset Sup.{A B} 1{A C} 2{A E} 1{B C} 2{B E} 3{C E} 2
C 2
I temset Sup.{B C E} 2
C 3
Itemset Sup.{A} 2{B} 3{C} 3{E} 3
L1
I temset Sup.{A C} 2{B C} 2{B E} 3{C E} 2
L 2
I temset Sup.{B C E} 2
L 3
T. P. T. P. HongHong
112112
Example
Association rules ConfidenceIF BC THEN E S(BCE)/S(BC)=2/2IF BE THEN C S(BCE)/S(BE)=2/3IF CE THEN B S(BCE)/S(CE)=2/2IF B THEN CE S(BCE)/S(B)=2/3IF C THEN BE S(BCE)/S(C)=2/3IF E THEN BC S(BCE)/S(E)=2/3IF A THEN C S(AC)/S(A)=2/2IF C THEN A S(AC)/S(C)=2/3IF B THEN C S(BC)/S(B)=2/3IF C THEN B S(BC)/S(C)=2/3IF B THEN E S(BE)/S(B)=3/3IF E THEN B S(BE)/S(E)=3/3IF C THEN E S(CE)/S(C)=2/3IF E THEN C S(CE)/S(E)=2/3
T. P. T. P. HongHong
113113
Integrating Mined Knowledge
Association Rules
A BB, C DA, C E
.
.
.
If customers buy A, then they will
buy B.
If customers buy B and C, then
they will buy D .
If customers buy A and C, then they will
buy E .
Branch 1
Branch 2
Branch 3
Headquarter
T. P. T. P. HongHong
114114
Integration of Association Rules
Xindong Wu and Shichao Zhang (2003)Synthesizing High-Frequency Rules fromDifferent Data Sources
Known data sources
AB→CA→DB→E
AB→CA→DB→E
DB1 DB2... DBn
RD1 RD2 RDn...
GRB Synthesizing High-Frequency Rules
• Weighting
• Ranking
AB→CA→DB→E
T. P. T. P. HongHong
115115
Integration of Association Rules (Cont.)
Xindong Wu and Shichao Zhang (2003)Synthesizing High-Frequency Rules fromDifferent Data Sources
Unknown data sources
Internet
Web books journals
X→Yconf=0.7
X→Yconf=0.72
X→Yconf=0.68
X→Yconf=?
Synthesizing• clustering method
T. P. T. P. HongHong
116116
Integration of Association Rules
Framework
Functions 1Membership
Fuzzy Rule Set 1
Data Mining Method
Data Mining Method
Transaction database 1
Functions iMembership
Fuzzy Rule Set i
Data Mining Method
Data Mining Method
Transaction database i
Functions nMembership
Fuzzy Rule Set n
Data Mining Method
Data Mining Method
Transaction database n
……
Intermediary representation
Intermediary representation
Intermediary representation
Knowledge IntegrationGenetic Fuzzy
Encoding
IntegrationSample Data
Fuzzy Rule Set+
Membership Functions
T. P. T. P. HongHong
117117
Data Mining Method
Mining Fuzzy Association Rules and Membership Functions
Chromosome1
MF Acquisition process
linguistic terms
Membership
Genetic Fuzzy
Fuzzy Miningfor Large 1-itemsets
Final Membership Function Set
PC
Minimum support Minimum confidence
… Function Set1
Membership Function Set2
Membership Membership Function Setq Function Set3
Chromosome2 Chromosome3 Chromosomeq…
Transaction
Database
Population
Fuzzy Mining
Fuzzy Association Rules
Mining Membership Functions
Mining Fuzzy Association Rules
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Mining Membership Functions
Examplemilk
5 10 15
Low Middle High
Quantity0
Membership value
bread
6 12 18
Low Middle High
Quantity0
Membership value
cookies
3 6 9
Low Middle High
Quantity0
Membership value
beverage
4 8 12
Low Middle High
Quantity0
Membership value
5, 5 10, 5 15, 5 6, 6 12, 6 18, 6 3, 3 6, 3 9, 3, , , , , ,
11 12 13 21 22 23 31 32 33R R R R R R R R R
MF1 MF2 MF3
4, 4 8, 4 12, 4, ,
41 42 43R R R
MF4
Low Middle High Low Middle High Low Middle High Low Middle High
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Fitness Function
Formally
The two bad kinds of membership functions
)C(ySuitabilit
|L|)C(f
q
1q
5 8 9
Low Middle High
Quantity0
(a)
5 20 25
Low Middle High
Quantity0
(b)
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Mining Fuzzy Association Rules
Our fuzzy mining algorithm (2001)
Trade-off between time complexity and number of rules for fuzzy mining from quantitative data
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Conclusions
Classification RulesA genetic knowledge-integration framework and four knowledge integration methodologies are proposed
GKIDSO ApproachTPGKI ApproachGFKILM ApproachGFKIGM Approach
Two real-world applications have been developed by our approaches
A self-integrating knowledge-based brain tumor diagnostic systemA sugar-cane breeding prediction system
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Conclusions (Cont.)
AdvantagesOnly a little computation time is neededA large number of rule sets can be effectively integratedIt is objectiveIt may find new knowledgeDomain experts need not intervene when conflict occurs
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Conclusions (Cont.)
DisadvantagesAll knowledge sources need pre-process to be represented by rule stringsIt need collect a set of data to measure the resulting knowledgeIf the derived knowledge sources are too few, the initial some dummy knowledge sources are inserted into the population
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Conclusions (Cont.)
Fuzzy Association Rulesfuzzy Mining + GA-based evolution
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Future Work
Heterogeneous knowledge representationVocabulary
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