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Method of classifier selection using the genetic approach
Author(s) -
Jackowski Konrad,
Wozniak Michal
Publication year - 2010
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2010.00513.x
Subject(s) - computer science , classifier (uml) , artificial intelligence , machine learning , voting , random subspace method , quadratic classifier , genetic algorithm , pattern recognition (psychology) , data mining , politics , political science , law
The paper presents a novel machine learning algorithm used for training a compound classifier system that consists of a set of area classifiers. Area classifiers recognize objects derived from the respective competence area. Splitting feature space into areas and selecting area classifiers are two key processes of the algorithm; both take place simultaneously in the course of an optimization process aimed at maximizing the system performance. An evolutionary algorithm is used to find the optimal solution. A number of experiments have been carried out to evaluate system performance. The results prove that the proposed method outperforms each elementary classifier as well as simple voting.