PARTITIONING OF THE FEATURE SET FOR CLASSIFIER COOPERATIONS
Dusan Cakmakov, Dejan Gorgevik
Abstract: In this paper, various cooperation schemes of SVM (Support Vector Machine) classifiers applied on two feature sets for handwritten digit recognition are examined. We start with a feature set composed of structural and statistical features and corresponding SVM classifier applied on the complete feature set. Later, we investigate the various partitions of the feature set as well as the advantages and weaknesses of various decision fusion schemes applied on SVM classifiers designed for partitioned feature sets. The obtained results show that it is difficult to exceed the recognition rate of a single SVM classifier applied straightforwardly on the complete feature set. Additionally, we show that the partitioning of the feature set according to feature nature (structural and statistical features) is not always the best way for designing classifier cooperation schemes. These results impose need of special feature selection procedures for optimal partitioning of the feature set for classifier cooperation schemes.