Integrating multicriteria PROMETHEE II method into a single-layer perceptron for two-class pattern classification
PROMETHEE methods based on the outranking relation theory are extensively used in multicriteria decision aid. A preference index representing the intensity of preference for one pattern over another pattern can be measured by various preference functions. The higher the intensity, the stronger the preference is indicated. In contrast to traditional single-layer perceptrons (SLPs) with the sigmoid function, this paper develops a novel PROMETHEE II-based SLP using concepts from the PROMETHEE II method involving pairwise comparisons between patterns. The assignment of a class label to a pattern is dependent on its net preference index, which the proposed perceptron obtains. Specially, this study designs a genetic-algorithm-based learning algorithm to determine the relative weights of respective criteria in order to derive the preference index for any pair of patterns. Computer simulations involving several real-world data sets reveal the classification performance of the proposed PROMETHEE II-based SLP. The proposed perceptron performs well compared to the other well-known fuzzy or non-fuzzy classification methods.
promethee method multicriteria decision aid classification problems genetic algorithm preference relation