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
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.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 International License.