Journal
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS
Volume 32, Issue 4, Pages 328-339Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCC.2002.806060
Keywords
classification; decision tree; fuzzy ID3; knowledge-based network; rule evaluation; rule generation; soft computing
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A fuzzy knowledge-based network is developed based on the linguistic rules extracted from a fuzzy decision tree. A scheme far automatic linguistic discretization of continuous attributes, based on quantiles, is formulated. A novel concept for measuring the goodness of a decision tree, in terms of its compactness (size) and efficient performance, is introduced. Linguistic rules are quantitatively evaluated using new indices. The rules are mapped to a fuzzy knowledge-based network, incorporating the frequency of samples and depth of the attributes in the decision tree. New fuzziness measures, in terms of class memberships, are used at the, node level of the tree to take care of overlapping classes. The effectiveness of the system, in terms of recognition scores, structure of decision tree, performance of rules, and network size, is extensively demonstrated on three sets of real-life data.
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