遺伝的アルゴリズムによる選言概念獲得アルゴリズム

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Inductive inference is one of the most active work fields of machine learning. Various methods of concepts learning from examples are proposed. The method of "Version Space" proposed by T. M. Mitchell is a typical method of the concept learning from training examples. The algorithm of inference mechanism in Version Space, called candidate-elimination algorithm, needs a complete inference process to obtain the correct solution. Therefore, the algorithm has some problems, such as(l) it is very difficult for the algorithm to acquire disjunctive hypotheses, (2) the algorithm cannot form "plausible" hypotheses during the inference, (3) the algorithm cannot form hypotheses, if training examples have some wrong one, called noise, (4) the algorithm cannot form hypotheses from only positive examples, and (5) the algorithm cannot form hypotheses, if representation language has changed during the inference. We pay attention on the combinatorial structure of concepts form, then apply to Genetic Algorithm originally proposed by J. Holland [Holland 75]. In this paper, the method of the concept learning based on Genetic Algorithm is proposed. The new method can acquire disjunctive concepts and form plausible concepts during the inference. Concepts acquired by new algorithm have few influences of the noisy training data. As for the new algorithm, it was experimentally confirmed that the concepts acquisition only from positive examples is possible.
社団法人人工知能学会の論文
1995-01-01

遺伝的アルゴリズムによる選言概念獲得アルゴリズム

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