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Research on an Optimized C4.5 Algorithm Based on Rough Set Theory

Zhuoyuan Xiang, Lei Zhang

Open publisher page 10 citations

Abstract

This paper proposes an improved algorithm based on the rough set theory and C4.5 decision tree. The algorithm uses rough set theory to reduce the attributes in the decision system, and uses the reduced data as the input of C4.5 algorithm to construct a decision tree. This article has put this new algorithm into practice, and the result of the experiment shows that the improved algorithm has higher efficiency and accuracy compared with the traditional C4.5 algorithm.

About this research paper

What this paper is about

This paper proposes an improved algorithm based on the rough set theory and C4.5 decision tree. The algorithm uses rough set theory to reduce the attributes in the decision system, and uses the reduced data as the input of C4.5 algorithm to construct a decision tree. This article has put this new algorithm into practice, and the result of the experiment shows that the improved algorithm has higher efficiency and accuracy compared with the traditional C4.5 algorithm.

Why it matters

OpenAlex reports 10 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

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Main findings

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Limitations

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Applications

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Available abstract

This paper proposes an improved algorithm based on the rough set theory and C4.5 decision tree. The algorithm uses rough set theory to reduce the attributes in the decision system, and uses the reduced data as the input of C4.5 algorithm to construct a decision tree. This article has put this new algorithm into practice, and the result of the experiment shows that the improved algorithm has higher efficiency and accuracy compared with the traditional C4.5 algorithm.

Key concepts: Rough set, Computer science, Decision tree, Algorithm, Construct (python library), Dominance-based rough set approach, Set (abstract data type), Set theory

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