A single-layer perceptron with PROMETHEE methods using novel preference indices

The Preference Ranking Organization METHods for Enrichment Evaluations (PROMETHEE) methods, based on the outranking relation theory, are used extensively in multi-criteria decision aid (MCDA). In particular, preference indices with weighted average aggregation representing the intensity of preference for one pattern over another pattern are measured by various preference functions. The higher the intensity, the stronger the preference is indicated. For MCDA, to obtain the ranking of alternatives, compromise operators such as the weighted average aggregation, or the disjunctive operators are often employed to aggregate the performance values of criteria. The compromise operators express the group utility or the majority rule, whereas the disjunctive operators take into account the strongly opponent or agreeable minorities. Since these two types of operators have their own unique features, it is interesting to develop a novel aggregator by integrating them into a single aggregator for a preference index. This study aims to develop a novel PROMETHEE-based single-layer perceptron (PROSLP) for pattern classification using the proposed preference index. The assignment of a class label to a pattern is dependent on its net preference index, which is obtained by the proposed perceptron. Computer simulations involving several real-world data sets reveal the classification performance of the proposed PROMETHEE-based SLP. The proposed perceptron with the novel preference index performs well compared to that with the original one.