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A Constructing Method of Decision Tree and Classification Rule Extraction for Incomplete Information System

Haifeng Yang, Zhihai Xu, Jifu Zhang, Jianghui Cai

Open publisher page 4 citations

Abstract

Decision tree is an effective way of classification rule extraction. For the incomplete decision system, an algorithm of constructing decision tree and classification rule extraction is presented based on the logical relationship between attributes which is described with generalized decision function, and conditional information entropy as heuristic. In the end, experimental results validate its correctness and efficiency by using star spectra data as the incomplete decision system.

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What this paper is about

Decision tree is an effective way of classification rule extraction. For the incomplete decision system, an algorithm of constructing decision tree and classification rule extraction is presented based on the logical relationship between attributes which is described with generalized decision function, and conditional information entropy as heuristic. In the end, experimental results validate its correctness and efficiency by using star spectra data as the incomplete decision system.

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OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Decision tree is an effective way of classification rule extraction. For the incomplete decision system, an algorithm of constructing decision tree and classification rule extraction is presented based on the logical relationship between attributes which is described with generalized decision function, and conditional information entropy as heuristic. In the end, experimental results validate its correctness and efficiency by using star spectra data as the incomplete decision system.

Key concepts: Decision tree, Incremental decision tree, Decision tree learning, Correctness, Computer science, Decision rule, Data mining, ID3 algorithm

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