Fuzzy pocket algorithm: a generalized pocket algorithm for classification of fuzzy inputs
Hahn-Ming Lee, Weng-Tang Wang · 2005
Perceptron algorithm has been widely adopted in pattern recognition to decide linear decision boundaries. Pocket algorithm, a perceptron-based algorithm, works well with nonseparable or even contradictory training instances. In this paper, a generalized pocket algorithm, called fuzzy pocket algorithm, that is capable of handling inputs in linguistics terms is proposed. Linguistic terms are represented as LR-type fuzzy sets. LR-type fuzzy sets operations and defuzzification method are utilized. The fuzzy pocket algorithm is suitable of both fuzzy and crisp inputs. Besides, nodes needed for a linguistic term are few and computation load is light. One sample problem, called knowledge-based evaluator, is considered to illustrate the working of the proposed method. Also, the experimental results are very encouraging.