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A fuzzy matching method of fuzzy decision trees

Junhyeong Lee, Juan Sun, Lanzhen Yang

Open publisher page 8 citations

Abstract

In this paper, we present a matching method that can improve the classification performance of a fuzzy decision tree (FDT). This method takes into consideration prediction strength of leave nodes of a fuzzy decision tree by combining true degrees (CF) of fuzzy rules, generated from a fuzzy decision tree, with membership degrees of antecedent parts of rules when applied to cases for classification. We illustrate the importance of CF through an example. An experiment shows by using this method, we can obtain more accurate results of classification when compared to the original method and to those obtained using the C5.0 decision tree.

About this research paper

What this paper is about

In this paper, we present a matching method that can improve the classification performance of a fuzzy decision tree (FDT). This method takes into consideration prediction strength of leave nodes of a fuzzy decision tree by combining true degrees (CF) of fuzzy rules, generated from a fuzzy decision tree, with membership degrees of antecedent parts of rules when applied to cases for classification. We illustrate the importance of CF through an example. An experiment shows by using this method, we can obtain more accurate results of classification when compared to the original method and to those obtained using the C5.0 decision tree.

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OpenAlex reports 8 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

In this paper, we present a matching method that can improve the classification performance of a fuzzy decision tree (FDT). This method takes into consideration prediction strength of leave nodes of a fuzzy decision tree by combining true degrees (CF) of fuzzy rules, generated from a fuzzy decision tree, with membership degrees of antecedent parts of rules when applied to cases for classification. We illustrate the importance of CF through an example. An experiment shows by using this method, we can obtain more accurate results of classification when compared to the original method and to those obtained using the C5.0 decision tree.

Key concepts: Decision tree, Fuzzy logic, Fuzzy classification, Matching (statistics), Antecedent (behavioral psychology), Data mining, Computer science, Incremental decision tree

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