1 Using Bayesian Network for combining classifiers Leonardo Nogueira Matos Departamento de...
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1 Using Bayesian Network for combining classifiers Leonardo Nogueira Matos Departamento de Computação Universidade Federal de Sergipe
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Transcript of 1 Using Bayesian Network for combining classifiers Leonardo Nogueira Matos Departamento de...
1
Using Bayesian Network for combining classifiers
Leonardo Nogueira MatosDepartamento de Computação
Universidade Federal de Sergipe
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Agenda
Why combining classifiers?
Bayesian network principles
Bayesian network as an ensemble of classifiers
Experimental results
Future works and conclusions
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Why combining classifiers?
Classifiers can colabore with each other
Minimizes computational effort for training
Maximizes global recognition rate
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Why not to do so?
Because combining individual preditions can be so difficult as divising a robust single classifier
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Why not to do so?
Because combining individual preditions can be so difficult as divising a robust single classifier
Decision
Classifiers
Combiner
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Approaches for combining classifiers
L1. Data Level L3. Decision Level L2. Feature Level
Fixed rules Trainable rules
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Approaches for combining classifiers
L1. Data Level L3. Decision Level L2. Feature Level
Fixed rules Trainable rules
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Why not to do so?
Because combining individual preditions can be so difficult as divising a robust single classifier
Decision
Classifiers
Combiner
p(w|x)
p(w|x)
p(w|x)
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Approaches for combining classifiers
L1. Data Level L3. Decision Level L2. Feature Level
Fixed rules Trainable rules
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Existent scenarios
Pattern space
Pattern21
classifiers
classifiers
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Our scenery
Pattern space
classifiers
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A closed look
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A closed look – discriminant function
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A closed look – using multiple classifiers
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A closed look – using multiple classifiers
The challegers:
How can we combine classifier's output?How can we identify regions in pattern space?
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Agenda
Why combining classifiers?
Bayesian network principles
Bayesian network as an ensemble of classifiers
Experimental results
Future works and conclusions
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Bayesian network principles
A
B C
Those circles represent binary random variables
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Bayesian network principles
A
B C
Those circles represent binary random variables
a0a1
b0b1
c0c1
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Bayesian network principles
A
B C
Those circles represent binary random variables
a0a1
b0b1
c0c1
a0 b0 c0
a1 b1 c1
⋮ ⋮ ⋮aN bN cN
dataset
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Bayesian network principles
A
B C
Those circles represent binary random variables
a0a1
b0b1
c0c1
a0 b0 c0
a1 b1 c1
⋮ ⋮ ⋮aN bN cNinstance
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Bayesian network principles
A
B C
Jointly probability inference is a combinatorial problem
P abc = P a P b∣a P c∣ab
2 possibilities
4 possibilities
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Bayesian network principles
A
B C
Jointly probability inference is a combinatorial problem
P abc = P a P b∣a P c∣ab
P abc = P a P b∣a P c
Independence makes computation alittle more simple
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Bayesian network principles
A
B C
Arest – indicates statistical dependence between variables
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Bayesian network principles
A
B C
Arc – represents causality
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Bayesian network principles
A
B C
A Bayesian network is a DAG (DirectAciclic Graph) where nodes representrandom variables and arcs representcausality relatioship
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Bayesian network principles
A
B C
There are polinomial time algorithmsto compute inference in BN
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Bayesian network principles
A
B C
There are polinomial time algorithmsto compute inference in BN
Evidence
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Bayesian network principles
A
B C
There are polinomial time algorithmsto compute inference in BN
Evidence messages
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Bayesian network principles
A
B C
There are polinomial time algorithmsto compute inference in BN
Evidence
[P a0∣bP a1∣b]
[P c0∣b P c1∣b ]
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Agenda
Why combining classifiers?
Bayesian network principles
Bayesian network as an ensemble of classifiers
Experimental results
Future works and conclusions
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A Fundamental Goal
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Another insight
From a statistical point-of-view a Bayesian network is also a graphicalmodel to represents a complex and factored probability distribution function
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Another insight
From a statistical point-of-view a Bayesian network is also a graphicalmodel to represents a complex and factored probability distribution function
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Another insight
From a statistical point-of-view a Bayesian network is also a graphicalmodel to represents a complex and factored probability distribution function
The challegers:
How can we combine classifier's output?How can we identify regions in pattern space?
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How can we combine classifier's output?
We use a BN as a graphical model of the pdf P(w|x)
We assume that classifier participate in computing that function
Each classifier must be a statistical classifier
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How can we identify regions in pattern space?
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Splitting pattern space
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Defining a region
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Patterns in a region
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Algorithm
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Bayesian Network Structure
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Bayesian networks for combining classifiers
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Agenda
Why combining classifiers?
Bayesian network principles
Bayesian network as an ensemble of classifiers
Experimental results
Future works and conclusions
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Results with UCI databases
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Results with NIST database
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System I classifiers
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Preliminaries
48
Results with the complete dataset
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Agenda
Why combining classifiers?
Bayesian network principles
Bayesian network as an ensemble of classifiers
Experimental results
Future works and conclusions
50
Future works
51
Future works
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Future works
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Future works
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Future works
Pattern space
Pattern21
classifiers
classifiers
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ConclusionsWe have developed a method for combining classifiers using a Bayesian network
A BN act as trainable ensemble of statistical classifiers
The method is not suitable for small size dataset
Experimental results reveal a good performance with a large dataset
As a future work we intend to use a similar approach for splitting the feature vector and combine classifiers specialized on each piece of it.
