Presented by Peter Duval

Rule Induction with Extension Matrices Dr. Xindong Wu

Journal of the American Society for Information ScienceVOL. 49, NO. 5, 1998

Presented by Peter DuvalS3 Elimination Strategy

EM

pathInevit

able Selector

HCV

NEM

PE

NE

EMD

HFL

Eliminable Selector

Redundant Selector

MFL

MCV

S1 Fast Strategy

S2 Precedence Strategy

S4 Least Frequency Selector

Persistent

Intersecting Group

Context

• HFL/HCV presents an alternative to decision trees with rule induction.

• HCV can be used as a benchmark for rule induction.

Context

• This paper condenses Dr. Wu’s Ph.D. dissertation on the extension matrix approach and HFL/HCV algorithm.

• Look to University of Illinois, J. Wong and R.S. Michalski for work leading to HFL/HCV.

• HFL/HCV appears to be underrepresented in literature citations.

Overview

1. Represent the negative training data as row vectors in a matrix.

2. Process positive examples as they come to eliminate uninformative attributes in the negative examples.

3. Read conjunctive rules from the resulting matrix.

4. Simplify and cleanup the rules.

• A positive example (PE):

Positive and Negative Examples

Play windy)high, mild, (overcast, )v ,...,(ve ak1kk

Playt Don’ windy)high, hot, (rainy, )v ,..., (ve ak1kk

• A negative example (NE):

Negative example matrix (NEM)

Gather the negative examples as row vectors in the NEM:

rainy hot high windy

rainy cool normal windy

sunny hot normal windy

sunny mild high windy

Positive Example (PE) Against NEM

A positive example written as a row vector:





overcast mild high windy

Extension Matrices

• Delete any matching elements in the NEM

windyhighmildsunny

windynormalhotsunny

windynormalcoolrainy

windyhighhotrainy

overcast mild high windy

Extension Matrices

• We construct one Extension Matrix per Positive Example

***

*

*

**

sunny

normalhotsunny

normalcoolrainy

hotrainy

Extension Matrices

Let’s make a second extension matrix:





calmnormalmildovercast

Extension Matrices

The second extension matrix:

windyhighsunny

windyhotsunny

windycoolrainy

windyhighhotrainy

*

*

*

Extension Matrices

Finally let’s make a third extension matrix:

windyhighmildsunny

windynormalhotsunny

windynormalcoolrainy

windyhighhotrainy

calmhighhotrainy

Extension Matrices

The third extension matrix:

windymildsunny

windynormalhotsunny

windynormalcool

windy

*

*

***

Dead Elements• Dead Elements, *, take the place of attributes

that fail to distinguish the negative example from the corresponding positive example.

calmhighhotrainy

windymildsunny

windynormalhotsunny

windynormalcool

windy

*

*

***

***

***

***

****

sunny

sunny

cool

Matrix Disjunction (EMD)• If there exists a dead element in any position of

the extension matrices, the EMD will have a dead element there, too.

***

*

*

**

sunny

normalhotsunny

normalcoolrainy

hotrainy

windyhighsunny

windyhotsunny

windycoolrainy

windyhighhotrainy

*

*

*

windymildsunny

windynormalhotsunny

windynormalcool

windy

*

*

***

“OR” the dead elements

***

**

**

**

sunny

hotsunny

coolrainy

hotrainy

Partitions• Once a dead row would be created, start a new EMD.

***

*

*

**

sunny

normalhotsunny

normalcoolrainy

hotrainy

windyhighsunny

windyhotsunny

windycoolrainy

windyhighhotrainy

*

*

*

***

***

***

****

sunny

sunny

cool

windymildsunny

windynormalhotsunny

windynormalcool

windy

*

*

***

Partition 1 Partition 2

…

***

**

**

**

sunny

hotsunny

coolrainy

hotrainy

Matrix Disjunction (EMD)• Let’s construct the EMD using just the first two

Extension Matrices.

***

*

*

**

sunny

normalhotsunny

normalcoolrainy

hotrainy

windyhighsunny

windyhotsunny

windycoolrainy

windyhighhotrainy

*

*

*


***

**

**

**

sunny

hotsunny

coolrainy

hotrainy

Matrix Disjunction (EMD)• The EMD has dramatically reduced the amount

of superfluous information.

***

*

*

**

sunny

normalhotsunny

normalcoolrainy

hotrainy

windyhighsunny

windyhotsunny

windycoolrainy

windyhighhotrainy

*

*

*


***

**

**

**

sunny

hotsunny

coolrainy

hotrainy

Paths• Choose one non-dead element from each row.

This is called a path.

• We can create paths in EMs and EMDs.

***

**

**

**

sunny

hotsunny

coolrainy

hotrainy

Path Cover ≡ Conjunctive Formula

• The path corresponds to a conjuctive formula expressed in variable-valued logic.

PlaytDonsunnyrainyOutlook _']],[[

***

**

**

**

sunny

hotsunny

coolrainy

hotrainy

Path = Cover ≡ Conjunctive Formula

PlaytDon

coolhoteTemperatur

sunnyOutlook

_'

]],[[

]][[

HFL

Wu developed HFL to find good rules. An algorithm with 4 strategies, it finds a compact disjunction of conjunctions:1. Fast

2. Precedence

3. Elimination

4. Least Frequency

01

101

10

101

010

11

1

X4 X3 X2 X1

HFL Strategies: Fast

X3≠1 covers all negative examples.

X3≠1 => positive class. We can stop processing.

1

01

10

01

10

1

X3 X2 X1

HFL Strategies: Precedence• [X1≠1] and [X3≠1] are inevitable selectors.• Record conjunction and label the rows as covered.• Below, a path is formed. All rows are covered. We are

done.

ClassNegative

X

X

_

]]1[3[

]]1[1[

ClassPositive

X

X

_

]]1[3[

]]1[1[

HFL Strategies: Elimination

• Redundant selectors in attribute X2 can be eliminated because non-dead X3 values cover all of the rows covered by X2.

• All elements in column X2 become dead elements.

01

101

10

101

010

11

X4 X3 X2 X1

01

11

1

11

01

11

X4 X3 X2 X1

10

0

10

0

10

1

X3 X2 X1

HFL Strategies: Least Frequency

• Attribute X1 selectors are least frequent and can be eliminated.

• Other strategies must be applied before applying Least Frequency again.

10

01

10

01

10

11

X3 X2 X1

HCV Algorithm

• HCV improves HFL:1. Partition the positive examples into intersecting

groups.2. Apply HFL on each partition3. OR the conjunctive formulae from each partition.

Well described in:http://www.cs.uvm.edu/~xwu/Publication/JASIS.ps See Wu’s 1993 Ph.D dissertation for more background:http://www.era.lib.ed.ac.uk/bitstream/1842/581/3/1993-xindongw.pdf

HCV Software

• Features many refinements and switches• Works with C4.5 data.• Can be run through a web interface:

HCV Online Interface• Is described in Appendix A of Wu’s textbook,

and online:HCV Manual

Golf

Rules for the 'Play' class (Covering 3 examples): The 1st conjunctive rule: [ temperature != { cool } ] ^ [ outlook != { sunny } ] -->

the 'Play' class (Positive examples covered: 3)

Rules for the 'Don't_Play' class (Covering 4 examples): The 2nd conjunctive rule: [ outlook != { overcast } ] ^ [ wind = { windy } ] --> the

'Don't_Play' class (Positive examples covered: 4)

The total number of conjunctive rules is: 2 The default class is: 'Don't_Play' (Examples in class: 4) Time taken for induction (seconds): 0.0 (real), 0.0

(user), 0.0 (system) Rule file or preprocessed test file not found. Skipping

deduction

HCV

• HCV is competitive with other decision tree and rule producing algorithms.

• HCV generally produces more compact rules.• HCV outputs variable-valued logic.• HCV handles noise and discretization.• HCV guarantees a “conjunctive rule for a

concept”.

Ideas

• Can HFL/HCV be applied to chess? Bratko did this with ID3. [Crevier 1993, 177]

• How can HCV be parallelized?• How does the extension matrix approach work in

closed-world situations?• Is HCV 2.0 a good candidate for automated

parameter tuning by genetic algorithm or other evolutionary technique?

The End.

• Presentation based on slides by Leslie Damon.• Questions?

Exam Questions

• Definitions:Extension Matrix: a matrix of negative examples as row

vectors, where, for a given positive example, elements that match the positive example are replaced with dead elements, denoted as ‘*’.

Dead Element: an element of a negative example which cannot be used to distinguish a given positive example from the negative example.

Path: a set of non-dead elements, one each from all of the rows of an extension matrix.

Exam Questions

• Four stages of HFL:1. Fast: A single attribute value that covers all rows

2. Precedence: Favor attributes that are the only non-dead element of a row.

3. Elimination: Get rid of redundant elements.

4. Least Frequency: Get rid of columns that cover where non-dead values cover the fewest rows.

See slides labeled “HFL Strategies”

Exam Questions

• The Pneumonia/Tuberculosis problem is worked through in the paper and Leslie Damon’s slides. Here is the EMD:

absent strip normal

hole fast

strip normal

absent fast

fast normal

* *

* * *

* * *

* * *

* * *

Documents

Presented by Peter Duval