Intuitionistic fuzzy kernel-matching pursuit ensemble algorithm based on hybrid selection strategy
LEI Ying-jie
YU Xiao-dong
WANG Rui
WANG Yi
Abstract：In order to improve the generalization ability of a classifier ensemble, we propose an intuitionistic fuzzy kernel-matching pursuit algorithm based on the hybrid selection strategy for target recognition to select a subset of optimal individuals from the given classifier ensemble. The basic idea of this algorithm is to produce a preliminary subset of classifiers by disturbing the training set and the feature space, and then trim this subset by eliminating the redundant classifiers based on k-means clustering algorithm and dynamically singling out classifiers with high differentiability from practical object recognition, making the size of the subset adaptively change according to the complexity of the objects and the expected accuracy of recognition be determined recursively. Experimental results show that the performance of the proposed algorithm is more flexible, efficient and accurate, with higher generalization, in comparison to other classifier selection methods.
Keywords：intuitionistic fuzzy kernel matching pursuitselective ensemblehybrid selection strategydiversity mea-suregeneralization performance
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 336-343 )