Improved Entropy based Fuzzy Classifier in Handwritten Gesture Recognition
نویسنده
چکیده
Many Fuzzy classifiers have been developed to identify the handwritten gestures. Handwritten gestures various from person-to-person and with mood and time .Since these rulebased classifiers which are incremental in nature, a large number of rules are generated for unseen inputs.Thus, the size of the rule base rises exponentially ,further introducing rule overfitting and misclassification error. The problem of rule base explosion has been handled by an Entropy based classifier which keeps only most promising rules in the rule base ,based on the threshold value.In this paper,we have shown experimentally that the efficiency of this entropy based classifier can be further improved by considering more linguistic variables or linguistic values or a combination of both..
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