Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17139
Title: A decision support tool for apparel coordination through integrating the knowledge-based attribute evaluation expert system and the T-S fuzzy neural network
Authors: Wong, WK 
Zeng, XH
Au, WMR
Keywords: Back Propagation
Decision support
Fuzzy neural networks
Genetic Algorithm
Particle swarm optimization
Issue Date: 2009
Publisher: Pergamon Press
Source: Expert systems with applications, 2009, v. 36, no. 2 part 1, p. 2377-2390 How to cite?
Journal: Expert systems with applications 
Abstract: 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.
URI: http://hdl.handle.net/10397/17139
ISSN: 0957-4174
EISSN: 1873-6793
DOI: 10.1016/j.eswa.2007.12.068
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