GP-based secondary classifiers

doi:10.1016/j.patcog.2004.06.010

GP-based secondary classifiers

2005-04-01

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DOI：10.1016/j.patcog.2004.06.010

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Genetic programming (GP) is used to evolve secondary classifiers for disambiguating between pairs of handwritten digit images. The inherent property of feature selection accorded by GP is exploited to make sharper decision between conflicting classes. Classification can be done in several steps with an available feature set and a mixture of stratesies. A classification strategy is presented in this paper. After the first step of the classification using the full feature-set-the high confidence recognition result will lead to an end of the recognition process. Otherwise a secondary classifier designed using a sub-set of the original feature set and the information available from the earlier classification step will help classify the input further. The feature selection mechanism employed by GP selects important features that provide maximum separability between classes under consideration. In this way, a sharper decision on fewer classes is obtained at the secondary classification stage. The full feature set is still available in both stages of classification to retain complete information. An intuitive motivation and detailed analysis using confusion matrices between digit classes is presented to describe how this strategy leads to improved recognition performance. In comparison with the existing methods. our method is aimed for increasing recognition accuracy and reliability. Results are reported for the BHA test-set and the NIST test-set of handwritten digits. (C) 2004 Published by Elsevier Ltd on behalf of Pattern Recognition Society.

Keywords:

handwritten digit recognition

feature selection

genetic programming

classification

secondary classifiers

GP-based secondary classifiers

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