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A New Discernibility Metric and Its Application on Pattern Classification and Feature Evaluation

Abstract : A novel evaluation metric is introduced, based on the Discernibility concept. This metric, the Distance-based Index of Discernibility (DID) aims to provide an accurate and fast mapping of the classification performance of a feature or a dataset. DID has been successfully implemented in a program which has been applied to a number of datasets, a few artificial features and a typical benchmark dataset. The results appear to be quite promising, verifying the initial hypothesis.
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Zacharias Voulgaris. A New Discernibility Metric and Its Application on Pattern Classification and Feature Evaluation. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. pp.27-35, ⟨10.1007/978-3-642-23960-1_4⟩. ⟨hal-01571488⟩

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