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论文类型：期刊论文

发表时间：2015-01-01

发表刊物：PATTERN RECOGNITION

收录刊物：SCIE、EI

卷号：48

期号：1

页面范围：50-59

ISSN号：0031-3203

关键字：Decision tree; Fuzzy classifier; Fuzzy rules; Fuzzy confidence

摘要：This paper presents a new architecture of a fuzzy decision tree based on fuzzy rules - fuzzy rule based decision tree (FRDT) and provides a learning algorithm. In contrast with "traditional" axis-parallel decision trees in which only a single feature (variable) is taken into account at each node, the node of the proposed decision trees involves a fuzzy rule which involves multiple features. Fuzzy rules are employed to produce leaves of high purity. Using multiple features for a node helps us minimize the size of the trees. The growth of the FRDT is realized by expanding an additional node composed of a mixture of data coming from different classes, which is the only non-leaf node of each layer. This gives rise to a new geometric structure endowed with linguistic terms which are quite different from the "traditional" oblique decision trees endowed with hyperplanes as decision functions. A series of numeric studies are reported using data coming from UCI machine learning data sets. The comparison is carried out with regard to "traditional" decision trees such as C4.5, LADtree, BFTree, SimpleCart, and NBTree. The results of statistical tests have shown that the proposed FRDT exhibits the best performance in terms of both accuracy and the size of the produced trees. (C) 2014 Elsevier Ltd. All rights reserved.