Optimized distance metrics for differential evolution based nearest prototype classifier

  • Authors:

  • David Koloseni;Jouni Lampinen;Pasi Luukka

  • Affiliations:

  • Laboratory of Applied Mathematics, Lappeenranta University of Technology, P.O. Box 20, FI-53851 Lappeenranta, Finland;Department of Computer Science, University of Vaasa, P.O. Box 700, FI-65101 Vaasa, Finland and Department of Computer Science, VSB-Technical University of Ostrava, 17 Listopadu 15, 70833 Ostrava-P ...;Laboratory of Applied Mathematics, Lappeenranta University of Technology, P.O. Box 20, FI-53851 Lappeenranta, Finland

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2012

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Abstract

In this article, we introduce a differential evolution based classifier with extension for selecting automatically the applied distance measure from a predefined pool of alternative distances measures to suit optimally for classifying the particular data set at hand. The proposed method extends the earlier differential evolution based nearest prototype classifier by extending the optimization process by optimizing not only the required parameters for distance measures, but also optimizing the selection of the distance measure it self in order to find the best possible distance measure for the particular data set at hand. It has been clear for some time that in classification, usual euclidean distance is often not the best choice, and the optimal distance measure depends on the particular properties of the data sets to be classified. So far solving this issue have been subject to a limited attention in the literature. In cases where some consideration to this is problem is given, there has only been testing with couple distance measure to find which one applies best to the data at hand. In this paper we have attempted to take one step further by applying a systematic global optimization approach for selecting the best distance measure from a set of alternative measures for obtaining the highest classification accuracy for the given data. In particular, we have generated pool of distance measures for the purpose and developed a model on how the differential evolution based classifier can be extended to optimize the selection of the distance measure for given data. The obtained results are demonstrating, and also confirming further on the earlier findings reported in the literature, that often some other distance measure than the most commonly used euclidean distance is the best choice. The selection of distance measure is one of the most important factor for obtaining best classification accuracy, and should thereby be emphasized more in future research. The results also indicate that it is possible to build a classifier that is selecting the optimal distance measure for the given data automatically. It is also recommended that the proposed extension the differential evolution based classifier is clearly efficient alternative in solving classification problems.