Classification of Class Overlapping Datasets by Kernel-mts Method
نویسندگان
چکیده
Class overlapping is one of the bottlenecks in data mining and pattern recognition, and affects the classification accuracy and generalization ability directly. In Mahalanobis-Taguchi System (MTS), the normal samples are used to construct reference space, while the abnormal samples are used to verify the validity of the reference space. If there is a class overlapping between the normal samples and the abnormal samples, the result of classification will be affected. In this paper, kernel function and Mahalanobis distance are combined to form the kernel Mahalanobis distance as an improved measurement scale of the MTS. Experimental results show that Kernel-MTS is suitable for class overlapping classification, and it provides better classification accuracy than the conventional methods.
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