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SONIA-Based Decision Neural Network for Preference Assessment with Incomplete Comparisons
Muhammad R. Widyanto*, Kazuhiko Kawamoto*, Benyamin Kusumoputro**, and Kaoru Hirota*
*Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama-city 226-8502, Japan
**Faculty of Computer Science, University of Indonesia, Depok Campus, West Java, Indonesia
Received:February 22, 2005Accepted:May 30, 2005Published:November 20, 2005
Keywords:decision making, preference assessment, incomplete comparison, neural networks
Abstract
To deal with the problem of incomplete comparisons in decision maker preference assessment, a SONIA (Self-Organized Network inspired by Immune Algorithm)-based Decision Neural Network (DNN) is proposed. B cell mutation of SONIA creates hidden units having diverse data characteristics that improve generalization capability. This mutation deals with a limited number of training data resulting from incomplete pair-wise comparisons by decision maker. Experiments on linear, Euclidean, and Lp-metric function as underlying decision maker preference show that SONIA-based DNN outperforms conventional DNN for decision maker preference assessment with incomplete comparisons.
Cite this article as:M. Widyanto, K. Kawamoto, B. Kusumoputro, and K. Hirota, “SONIA-Based Decision Neural Network for Preference Assessment with Incomplete Comparisons,” J. Adv. Comput. Intell. Intell. Inform., Vol.9 No.6, pp. 607-614, 2005.Data files:
