A Modified K-Nearest Neighbor Classifier to Deal with Unbalanced Classes

INSTICC - Institute for Systems and Technologies of Information, Control and Communication
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Conference Proceeding
International Conference on Neural Computation (ICNC 2009), 2009, pp. 408 - 413
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We present in this paper a simple, yet valuable improvement to the traditional k-Nearest Neighbor (kNN) classifier. It aims at addressing the issue of unbalanced classes by maximizing the class-wise classification accuracy. The proposed classifier also gives the option of favoring a particular class through evaluating a small set of fuzzy rules. When tested on a number of UCI datasets, the proposed algorithm managed to achieve a uniformly good performance.
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