A decision support tool for apparel coordination through integrating the knowledge-based attribute evaluation expert system and the T–S fuzzy neural network

In today’s competitive fashion retailing business, providing “mix-and-match” or “fashion coordination” recommendations can enhance customer service, brand loyalty and improve sales. In this study, we propose a decision support tool for fashion coordination through the integration of the knowledge-based attribute evaluation expert system and the Takagi–Sugeno fuzzy neural network (TSFNN). A set of attributes of the apparel items for coordination are identified and formulated. The evaluation of these attributes can be accomplished by a knowledge-based expert system which can handle the difficulty of processing linguistic and categorical information effectively. A fuzzy clustering technique and a new hybrid learning algorithm combining the PSO and GA techniques are proposed to reduce the coordination rules and the training time for the TSFNN. The experimental results show that rules reduction can shorten the TSFNN training time while keeping a very satisfactory and low MSE value. The proposed hybrid algorithm outperforms the Back Propagation, the Genetic Algorithm, and the Particle Swarm Optimization. The apparel pairs recommended by the decision support tool are now integrated with a smart dressing system of a fashion retailing company in Hong Kong and practically used.