Yushi Jing GVU, College of Computing, Georgia Institute of Technology Vladimir Pavlovi ć

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Boosted Augmented Naive BayesEfficient discriminative learning of

Bayesian network classifiers

Yushi Jing GVU, College of Computing, Georgia Institute of Technology

Vladimir PavlovićDepartment of Computer Science, Rutgers University

James M. RehgGVU, College of Computing, Georgia Institute of Technology

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Contribution 1. Boosting approach to Bayesian network classification

o Additive combination of simple models (e.g. Naïve Bayes)

o Weighted maximum likelihood learning

o Generalizes Boosted Naïve Bayes (Elkan 1997)o Comprehensive experimental evaluation of BNB.

2. Boosted Augmented Naïve Bayes (BAN)

o Efficient training algorithm

o Competitive classification accuracyo Naïve Bayes, TAN, BNC (2004), ELR (2001)

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Bayesian network Modular and Intuitive graphical representation

Explicit Probabilistic Representation

Bayesian network classifiers Joint distribution Conditional distribution Class Label

How to efficiently train Bayesian network discriminatively to improve its classification accuracy?

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Parameter Learning

...................

Maximum Likelihood parameter learning Efficient parameter learning algorithm Maximizes LLG score

No analytic solution for parameters that maximizes CLLG

CLL

1 1

log ( | ) log ( )M M

i i i

i i

LL P y x P x

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Model selection

ML does not optimize CLLA

ELRA optimizes CLLA

(Greiner and Zhou, 2002)

ML optimizes CLLB when B is optimal

BNC algorithm searches for the

optimal structure (Grossman and Domingos, 2004)

A

B

C Ensemble of sparse model as an alternative to B Using ML to train each sparse model

Excellent classification accuracy

Computationally expensive in training

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Talk outline

o Minimization function for Boosted Bayesian networko Empirical Evaluation of Boosted Naïve Bayeso Boosted Augmented Naïve Bayes (BAN)o Empirical Evaluation of BAN

Our Goal:

o Combine parameter and structure optimization

o Avoid over-fitting

o Retain training efficiency

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Exponential Loss Function (ELF)

Boosted Bayesian network classifier minimizes ELF function.

1

1 exp{ 2 ( )}yF x

( | )FP y x

1

1 ( | )1exp log

2 ( | )

i iMF

F i ii F

P y xELF

P y x

1

exp ( )M

i iF

i

ELF y F x

1

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( | )

M

i ii FP y x

ELFF is an upper bound of –CLLF

1

( ) ( )k

K

kk

F x f x

where

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Minimizing ELF via ensemble method Ensemble method

Adaboost (Population version) constructs F(x) additively to approximately minimizes ELFF

Discriminatively updates the data weights

Tractable ML learning to train the parameters

1( )f x

i

2( )f x

1

( ) ( )k

K

kk

F x f x

3( )f x

…

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Results: 25 UCI datasets (BNB)

BNB vs. NB 0.151 vs.

0.173

BNB (10)

NB (2)

(13)

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Results: 25 UCI datasets (BNB)

BNB vs. NB 0.151 vs.

0.173

BNB vs. ELR-NB

0.151 vs. 0.161

BNB vs. TAN 0.151 vs. 0.184

BNB vs. BNC-2P0.151 vs. 0.164

BNB (9)

TAN (2)

BNB (5*)

ELR-NB (4*)

BNB (7)

BNC-2P (3)

BNB (10)

NB (2)

(13) (14)

(16) (15)

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Evaluation of BNB Computationally Efficient method

O(MNT) , T = 5~20, O(MN)

Good classification Accuracy Outperforms NB, TAN Competitive with ELR, BNC Sparse structure + boosting = competitive accuracy

Potential drawbacks Strongly correlated features (Corral, etc)

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Structure Learning Challenge:

Efficiency NP-hard problem

K-2, Hill Climbing search still examines polynomial number of structures

Resisting overfitting Structure controls classifier capacity

Our proposed solution: Combines sparse model to form an ensemble

Constrains edge selection

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Creating Step 1 (Friedman et al. 1999)

Build pair-wise conditional mutual information table

Create maximum spanning tree using conditional mutual information as edge weight

Convert a undirected graph into a directed graphtreeG

treeG4

3

21

treeG

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Initial structure

BANG

treeG

4

3

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1. Select Naïve Bayes

2. Create BNB via AdaBoost

3. Evaluate BNB

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Iteratively adding edges

BANG

treeG

4

3

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Ensemble CLL = -0.65

Ensemble CLL = -0.75

Ensemble CLL = -0.50

Ensemble CLL = -0.55?

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Final BAN structure

BANGEnsemble of the final structure produced by

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Analysis of BAN BAN

The base structure is sparser than BNC model

BAN uses an ensemble of sparser models to approximate a densely connected structure

Y

X

Example of BAN model Example of BNC-2P model

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Computational complexity of BAN Training Complexity: O(MN^2+ MNTS)

O (MN^2) G_tree O (MNTS) Structure Search

T => boosting iteration per structure S => number of structure examined S < N

Empirical training time T = 5~25, S = 0~5 Approximately 25-100 times the training of NB

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Result (simulated dataset):

25 different distribution CPT table Number of features

4000 samples, 5 fold cross validation

True structure:

Y

X

Naïve Bayes:

Y

X

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Results: (simulated dataset):

BAN(19)

NB (0)

(6)

BAN VS NB

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BNB (0)

Results: (simulated dataset):

BAN VS BNB

BAN (3)

• Correct edges added under BAN

22 True structure:

BNB achieved optimal error in 22 datasets

BAN outperforms BNB in the remaining 3

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Results: 25 UCI datasets (BAN)

Standard datasets for Bayesian network classifiers Friedman et. al. 1999 Greiner and Zhou 2002 Grossman and Domingos 2004

5 fold cross validation Implemented NB, TAN, BAN, BNB, BNC-2P Obtained results for ELR-NB, ELR-TAN

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BAN VS NB 0.141 VS 0.173

BAN VS TAN 0.141 VS 0.184

BAN (10)

NB (2)

BAN (10)

TAN (2)

(13)

Results: BAN vs. Standard method

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BAN VS BNC-2P 0.141 VS 0.164

BAN (7)

BNC (1)

Results: BAN vs. Structure Learning

BAN contains 0-5 augmented edges BNC-2P contains 4-16 augmented

edges

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BAN VS ELR-TAN 0.141 vs. 0.155

BAN VS ELR-NB 0.141 vs. 0.161

(13)

BAN (4)*

BAN (5)*

BAN (6)*

Error stats directly taken from published results

BAN is more efficient to train

Results: BAN vs. ELR

BAN (8)* (14)

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Evaluation of BAN vs. BNB

BAN VS BNB 0.141 VS 0.151

Comparison under significance test BAN outperforms BNB (7)

Corral 2% - 5%

BNB outperforms BAN (2) 0.5%-2%

Not significant 13 BAN choose BNB as base structure

IRIS, MOFN

Average testing error 0.141 vs. 0.151 BAN outperforms BNB (16) BNB outperforms BAN (6)

BAN (7)

BNB (2)

(14)

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Conclusion An ensemble of sparse model as an alternative to

structure and parameter optimization Simple to implement Very efficient in training Competitive classification accuracy

NB, TAN, HGC BNC ELR

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Future Work Extend BAN to handle sequential data

Analyze the class of Bayesian network classifiers that can be approximated with an ensemble of sparse structures.

Can the BAN model parameters be obtained through parameter learning given the final model structure?

Can we use BAN approach to learn generative models?