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Data-driven decision tree learning algorithm based on rough set theory

Desheng Yin, Guoyin Wang, Yu Wu

Open publisher page 6 citations

Abstract

Decision tree pre-pruning is an effective method to solve the over-fitting problem in decision tree learning process. However, it is difficult to estimate the exact time to stop the growing process of a decision tree, which limits the developments and applications of this method. In this paper, the growing of a decision tree is controlled by the uncertainty of a decision table, and a data-driven learning algorithm for decision tree pre-pruning is developed.

About this research paper

What this paper is about

Decision tree pre-pruning is an effective method to solve the over-fitting problem in decision tree learning process. However, it is difficult to estimate the exact time to stop the growing process of a decision tree, which limits the developments and applications of this method. In this paper, the growing of a decision tree is controlled by the uncertainty of a decision table, and a data-driven learning algorithm for decision tree pre-pruning is developed.

Why it matters

OpenAlex reports 6 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Method / approach

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Main findings

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Available abstract

Decision tree pre-pruning is an effective method to solve the over-fitting problem in decision tree learning process. However, it is difficult to estimate the exact time to stop the growing process of a decision tree, which limits the developments and applications of this method. In this paper, the growing of a decision tree is controlled by the uncertainty of a decision table, and a data-driven learning algorithm for decision tree pre-pruning is developed.

Key concepts: Incremental decision tree, ID3 algorithm, Decision tree, Pruning, Decision tree learning, Computer science, Decision stump, Decision table

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