| Summary: | 碩士 === 國立臺灣科技大學 === 電子工程技術研究所 === 86 === This thesis presents an efficient fuzzy classifier with the ability of feature selection based on fuzzy entropy measure. The fuzzy entropy is employed to evaluate the information of pattern distribution in the pattern space. With such information, we can apply it to partition the pattern space into non-overlapped decision regions for pattern classification. Since the decision regions do not overlap, the complexity and computational load of the classifier are reduced and thus the training time and classification time are extremely fast. Although the decision regions are partitioned as non-overlapped subspaces, we can also achieve good performance by the produced smooth boundaries since the decision regions are fuzzy subspaces. In addition, we also investigate a fuzzy entropy-based method to select the relevant features. The feature selection procedure not only reduces the dimension of a problem but also discards the noise-corrupted, redundant or unimportant features. As a result, the time consuming of the classifier is reduced whereas the classification performance is increased. Finally, we apply the proposed classifier on the Iris database and Wisconsin breast cancer database to evaluate the classification performance. Both of the results show that the proposed classifier can work well for the pattern classification applications.
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