An approach for multi-objective categorization based on the game theory and Markov process

Realistic objects are not only described by their own attributes, but also described by their mutual relationships in a specific domain. By mainly considering the mutual associations among the given objects, in this paper we propose a method for multi-objective categorization based on the game theory and Markov process.We adopt Shapley value in coalitional games tomeasure the player’s satisfaction degree in a group. We then give the concept of priority groups and an algorithm to combine small-size priority groups to large-size ones, and thus the efficiency of calculating the players’ satisfaction degree can be improved. We further define a improving-replay Markov process to model the process of forming a reasonable payoff configuration. Accordingly, we give a simulation algorithm to obtain the desired payoff configuration to categorizeplayers into groupsby their satisfactiondegrees. Finally,wegive experimental results and performance studies to verify the efficiency and effectiveness of our methods. © 2011 Elsevier B.V. All rights reserved.