Improvement of ID3 algorithm based on simplified information entropy and coordination degree

被引：3

作者：

Wang Y. ^{[1
]}

Li Y. ^{[1
]}

Song Y. ^{[2
]}

Rong X. ^{[1
]}

Zhang S. ^{[3
]}

机构：

[1] School of Control Science and Engineering, Shandong University, Jinan

[2] School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai

[3] Department of Electrical Engineering and Information Technology, Shandong University of Science and Technology, Jinan

来源：

Algorithms | 2017年 / 10卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Coordination degree; Decision tree; ID3; algorithm; Information entropy;

D O I：

10.3390/a10040124

中图分类号：

学科分类号：

摘要：

The decision tree algorithm is a core technology in data classification mining, and ID3 (Iterative Dichotomiser 3) algorithm is a famous one, which has achieved good results in the field of classification mining. Nevertheless, there exist some disadvantages of ID3 such as attributes biasing multi-values, high complexity, large scales, etc. In this paper, an improved ID3 algorithm is proposed that combines the simplified information entropy based on different weights with coordination degree in rough set theory. The traditional ID3 algorithm and the proposed one are fairly compared by using three common data samples as well as the decision tree classifiers. It is shown that the proposed algorithm has a better performance in the running time and tree structure, but not in accuracy than the ID3 algorithm, for the first two sample sets, which are small. For the third sample set that is large, the proposed algorithm improves the ID3 algorithm for all of the running time, tree structure and accuracy. The experimental results show that the proposed algorithm is effective and viable. © 2017 by the authors.

引用

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