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Transformation-based hierarchical decision rules using genetic algorithms and its application to handwriting recognition domain

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5 Author(s)
Tonghua Su ; Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol., Harbin ; Tianwen Zhang ; Hujie Huang ; Guixiang Xue
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This paper describes a new approach based on transformation-based learning for extracting hierarchical decision rules. Genetic algorithms are adapted to establish the context environment for transformation operation and the transformation operation can lengthen the life cycle of "good" candidate rules. The experiments are conducted on iris, wine and glass datasets with a 10-fold cross validation setup. The results show that transformation operation can improve the precision of the classifier with a smaller number of rules and generations than hierarchical decision rules. The approach also works well in touching block extraction of Chinese handwritten text.

Published in:

Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on

Date of Conference:

1-6 June 2008