Paper Info
Title
Adaptive Splitting and Selection Method of Classifier Ensemble Building
Abstract
The paper presents a novel machine learning method which allows obtaining compound classifier. Its idea bases on splitting feature space into separate regions and choosing the best classifier from available set of recognizers for each region. Splitting and selection take place simultaneously as a part of an optimization process. Evolutionary algorithm is used to find out the optimal solution. The quality of the proposed method is evaluated via computer experiments.
Year
DOI
Venue
2009
10.1007/978-3-642-02319-4_63
HAIS
Keywords
Field
DocType
adaptive splitting,classifier ensemble building,novel machine,best classifier,splitting feature space,optimal solution,compound classifier,idea base,evolutionary algorithm,available set,computer experiment,selection method,feature space,machine learning
Computer experiment,Classifier fusion,Feature vector,Margin (machine learning),Evolutionary algorithm,Pattern recognition,Computer science,Artificial intelligence,Classifier (linguistics),Margin classifier,Machine learning,Quadratic classifier
Conference
Volume
ISSN
Citations 
5572
0302-9743
2
PageRank 
References 
Authors
0.37
12
2
Name
Order
Citations
PageRank
Konrad Jackowski113610.46
Michal Wozniak276483.90