Differential evolution based nearest prototype classifier with optimized distance measures for the features in the data sets

  • Authors:

  • David Koloseni;Jouni Lampinen;Pasi Luukka

  • Affiliations:

  • Laboratory of Applied Mathematics, Lappeenranta University of Technology, P.O. Box 20, FI-53851 Lappeenranta, Finland and University of Dar es salaam, Department of Mathematics, P.O. Box 35062, Da ...;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- ...;Laboratory of Applied Mathematics, Lappeenranta University of Technology, P.O. Box 20, FI-53851 Lappeenranta, Finland and School of Business, Lappeenranta University of Technology, P.O. Box 20, FI ...

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2013

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Abstract

In this paper a further generalization of differential evolution based data classification method is proposed, demonstrated and initially evaluated. The differential evolution classifier is a nearest prototype vector based classifier that applies a global optimization algorithm, differential evolution, for determining the optimal values for all free parameters of the classifier model during the training phase of the classifier. The earlier version of differential evolution classifier that applied individually optimized distance measure for each new data set to be classified is generalized here so, that instead of optimizing a single distance measure for the given data set, we take a further step by proposing an approach where distance measures are optimized individually for each feature of the data set to be classified. In particular, distance measures for each feature are selected optimally from a predefined pool of alternative distance measures. The optimal distance measures are determined by differential evolution algorithm, which is also determining the optimal values for all free parameters of the selected distance measures in parallel. After determining the optimal distance measures for each feature together with their optimal parameters, we combine all featurewisely determined distance measures to form a single total distance measure, that is to be applied for the final classification decisions. The actual classification process is still based on the nearest prototype vector principle; A sample belongs to the class represented by the nearest prototype vector when measured with the above referred optimized total distance measure. During the training process the differential evolution algorithm determines optimally the class vectors, selects optimal distance metrics for each data feature, and determines the optimal values for the free parameters of each selected distance measure. Based on experimental results with nine well known classification benchmark data sets, the proposed approach yield a statistically significant improvement to the classification accuracy of differential evolution classifier.