An Improved ID3 Decision Tree Algorithm Based on Attribute Weighted

DOI

10.2991/cmes-15.2015.167How to use a DOI?

Keywords

decision tree; ID3 algorithm; information gain

Abstract

ID3 decision tree algorithm uses information gain selection splitting attribute tend to choose the more property values, and the number of attribute values can not be used to measure the attribute importance, in view of the above problems, a new method is proposed for attribute weighting, the idea of simulation conditional probability, calculation of the close contact between the attributes and the decision attributes, as the attribute weights and combination with attribute information gain to selection splitting attribute, improve the accuracy of decision results. Experiments show that compared with the improved algorithm and the traditional ID3 algorithm, decision tree model has higher predictive accuracy, less number of leaves.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Xian Liang
AU  - Fuheng Qu
AU  - Yong Yang
AU  - Hua Cai
PY  - 2015/04
DA  - 2015/04
TI  - An Improved ID3 Decision Tree Algorithm Based on Attribute Weighted
BT  - Proceedings of the 2nd International Conference on Civil, Materials and Environmental Sciences
PB  - Atlantis Press
SP  - 613
EP  - 615
SN  - 2352-5401
UR  - https://doi.org/10.2991/cmes-15.2015.167
DO  - 10.2991/cmes-15.2015.167
ID  - Liang2015/04
ER  -