Evolving fuzzy case-based reasoning in wholesaler's returning book forecasting

ABSTRACT

This paper proposes a hybrid system that is developed by evolving Fuzzy Case-Based Reasoning (FCBR) with Genetic Algorithm (GA), for reverse sales forecasting of returning books. FCBR systems have been successfully applied in several domains of artificial intelligence. However, in conventional FCBR method each factor has the same weight which means each one has the same influence on the output data that does not reflect the practical situation. In order to enhance the efficiency and capability of forecasting in FCBR systems, we connected the GAs method to adjust the weights of factors in FCBR systems, GAFCBR for short. The case base of this research is acquired from a book wholesaler in Taiwan, and it is applied by the hybrid system to forecast returning books. The results of the prediction of the hybrid system were compared with the results of a back propagation neural network (BPNN), a conventional CBR, and a multiple-regression analysis method. The experimental results show that the GAFCBR is more accurate and efficient when being applied to the forecast of the returning books than other methods.

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Index Terms

• Applied computing

  • Enterprise computing

• Computing methodologies

  • Artificial intelligence

    • Knowledge representation and reasoning

      • Probabilistic reasoning

      • Vagueness and fuzzy logic