Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
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There are fewer effective methods to accurately discriminate the coronary microcirculatory dysfunction from the normal coronary microcirculation. Rather than traditional approaches only considering a single hemodynamic parameter, a novel scheme is proposed based on the generalized relevance learning vector quantization (GRLVQ) using multiple parameters (features). Naturally integrating the tasks of feature selection and classification, this scheme circularly adopts GRLVQ to gradually prune the unimportant features according to their weighting factors. In each circulation, the prototypes are generated for classification and the classification accuracy is obtained. Finally, the feature subset with the highest classification accuracy is selected and the corresponding classifier is also achieved. This approach not only simplifies the classifier but also enhances the classification performance. The method is verified on the physiological data collected from animals, and proved to be superior to the traditional single-parameter method.