Derivative free stochastic discrete gradient method with adaptive mutation

  • Authors:

  • Ranadhir Ghosh;Moumita Ghosh;Adil Bagirov

  • Affiliations:

  • School of Information Technology and Mathematical Sciences, University of Ballarat, Ballarat, Australia;School of Information Technology and Mathematical Sciences, University of Ballarat, Ballarat, Australia;School of Information Technology and Mathematical Sciences, University of Ballarat, Ballarat, Australia

  • Venue:
  • ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
  • Year:
  • 2006

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Abstract

In data mining we come across many problems such as function optimization problem or parameter estimation problem for classifiers for which a good learning algorithm for searching is very much necessary. In this paper we propose a stochastic based derivative free algorithm for unconstrained optimization problem. Many derivative-based local search methods exist which usually stuck into local solution for non-convex optimization problems. On the other hand global search methods are very time consuming and works for only limited number of variables. In this paper we investigate a derivative free multi search gradient based method which overcomes the problems of local minima and produces global solution in less time. We have tested the proposed method on many benchmark dataset in literature and compared the results with other existing algorithms. The results are very promising.