FUSION - Pattern Recognition, Classification, Classifier Fusion

By Dr. S Mishra, HoD, Dept of CE, I²IT
FUSION

Contents
Pattern Recognition
Classification
Classifier Fusion
International Institute of Information Technology, I²IT, P-14, Rajiv Gandhi Infotech Park, MIDC Phase 1, Hinjawadi, Pune - 411 057.
Tel +91 20 22933441 | www.isquareit.edu.in | Email info@isquareit.edu.in

How to select an algorithm
• Pattern recognition is a branch of machine learning that focuses on
the recognition of patterns and regularities in data, although it is in
some cases considered to be nearly synonymous with machine
learning.

• Classifier Fusion
Classifier Fusion was first proposed by
Dasarathy and Sheela’s in 1979.
The main idea of fusion is to combine a set
of models each of which solves the same
original task in order to obtain a better model
with more accuracy.

Classifier fusion consists of a set of individual classifiers i.e. a
fusion/selection method to combine/select individual classifier
outputs to give a final decision.
Types of ensemble:
Classifier Selection
Classifier Fusion

Functional Aspects of Fusion
A natural move when trying to solve numerous complicated
patterns.
 Efficiency
oDimension;
oSpeed
Accuracy

Importance of Classifier Fusion
Better classification performance than individual classifiers.
Beside avoiding the selection of the worse classifier under particular hypothesis,
fusion of multiple classifiers can improve the performance of the best individual
classifiers.
This is possible if individual classifiers make different errors.
For linear combiners, averaging the outputs of individual classifiers with unbiased
and uncorrelated errors can improve the performance of the best individual classifier.

Issues
Time complexity
Space Complexity
The new classifier might not be better than the single best classifier but it will distinguish
or eliminate the risk of picking an inadequate single classifier.
The final decision will be wrong if the output of selected classifier is wrong.
The trained classifier may not be competent enough to handle the problem.

Random Selection and k- Fold Cross
Validation
Data Set
n x m
Training
Testing Data
p x m
Training Data
t x m
Split the data set such that n=p + t
1 2 3 ... k
. . . .
. . . .
. . . .
SVM
Bayesian Classifier
MLP
Testing
Majority Voting

Gene
Expression
Data
ANN
Decreasing order
confidence matrix
Uniform Voting
Distribution Summation
Dempster-Shafer
Entropy Weighting
Density Based Weighting
Figure 2. Schematic representation of classifier fusion system
Principal Component
Analysis
Factor Analysis FLANN
Preprocessing Techniques
Support Vector Machine
Decision tree
k nearest neighbor
Bayesian Network
Classifiers
Class Ranking
Output
Classifier Fusion

References
[1]
[1]LZhenyu chen, Jianping Li, Liwei Wei, Weixuan Xu, Yong Shi: Multiple
– kernel SVM based multiple-task oriented data mining system for
gene expression data analysis, Expert Systems with Applications, Vol-
38, pp.12151-12159 (2011).
[2] Esma Kilic, Ethem Alpaydin: Learning the areas of expertise of
classifiers in an ensemble, Procedia Computer Science,Vol-3, pp.74-82
(2011).
[3] Nicolas Garcia – Pedrajas, Bomingo Ortiz – Boyer: An empirical study
of binary classifier fusion methods for multi class classification,
Information fusion ,Vol-12, pp.111-130 (2011).
[4] Hui-Min Feng, Xue-Fei li, Jun-Fen Chen: A comparative study of four
fuzzy integrals for classifier fusion, IEEE international Conference on
Machine learning and Cybernetics, pp.332-338 (2010).
[5] Jiangtao Huange, Minghui Wang, Bo Gu, Zhixiang Chen: Multiple
classifier combination based on interval-valued fuzzy permutation,
Journal of Computational Information Systems, Vol-6, pp.1759-1768
(2010).

THANK YOU !!
For further information please contact
Dr. S. Mishra
Department of Computer Engineering
Hope Foundation’s International Institute of Information Technology, I2IT
Hinjawadi, Pune – 411 057
Phone - +91 20 22933441
www.isquareit.edu.in | hodce@isquareit.edu.in

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