DocumentCode :
3494923
Title :
Bio-inspired meta-heuristic as feature selector in ensemble systems: A comparative analysis
Author :
Santana, Laura E. ; Canuto, Anne M P ; Silva, Ligia
Author_Institution :
Inf. & Appl. Math. Dept., Fed. Univ. of Rio Grande do Norte (UFRN), Natal, Brazil
fYear :
2011
fDate :
July 31 2011-Aug. 5 2011
Firstpage :
1112
Lastpage :
1119
Abstract :
Committees of classifiers, also known as ensemble systems, are composed of individual classifiers, organized in a parallel way and their output are combined in a combination method, which provides the final output of the system. In the context of these systems, feature selection methods can be used to provide different subsets of attributes for the individual classifiers, aiming to reduce redundancy among the attributes of a pattern and to increase the diversity in such systems. Since the problem of feature selection can be reduced to a search problem and that an exhaustive search for the subsets of attributes can be considered NP-hard, heuristic search can be adopted for solving this problem. This paper aims to introduce two important optimization techniques (Ant-colony and particle swarm) as a method to select attributes in an ensemble system as well as to compare their performance with Genetic Algorithm, whose research is well established in this area. These three algorithms have in common the fact that they bio-inspired meta-heuristics, since their search rules aim to simulate some aspects of the behavior of living beings.
Keywords :
feature extraction; learning (artificial intelligence); particle swarm optimisation; pattern classification; redundancy; search problems; set theory; NP-hard problem; ant colony optimization; attribute selection; bioinspired meta-heuristic; ensemble system; exhaustive search; feature selection method; particle swarm optimization; pattern classifier; redundancy reduction; search problem; search rules; subsets; Accuracy; Biological cells; Birds; Classification algorithms; Correlation; Genetic algorithms; Optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2011 International Joint Conference on
Conference_Location :
San Jose, CA
ISSN :
2161-4393
Print_ISBN :
978-1-4244-9635-8
Type :
conf
DOI :
10.1109/IJCNN.2011.6033348
Filename :
6033348
Link To Document :
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