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Automatic Knowledge Acquisition in the Form of Fuzzy Rules From Cases for Solving Classification Problem
Abstract
The authors consider an approach to automatic knowledge acquisition through machine learning on the basis of integrating the two basic reasoning methods – case-based reasoning and rule-based reasoning. Case-based reasoning allows using high-performance database technology for storing and accumulating cases, while rule-based reasoning is the most developed technology for creating declarative knowledge base on the basis of strong logical approach. This allows realizing the transformation of the spiral of knowledge, leading to continuous improvement of the knowledge quality in the management system. In the chapter, they propose one method of obtaining rules from cases based on fuzzy logic. Here the method is considered for solving classification problem, but it also can be applied for solving regression problem. The research shows acceptable accuracy of cases classification even for small training samples. At the same time, smoother (quadratic) membership functions show on average classification accuracy.
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