The Study On A Decision Tree Based On The Classification Preference Ratio

DOI

10.2991/amcce-15.2015.298How to use a DOI?

Keywords

decision tree; hierarchical granularity; classification preference ratio; condition attribute

Abstract

The decision tree is an important data mining method for classification. Granular computing has been applied to the decision tree, then a new decision tree based on the classification preference ratio of attribute is proposed. This decision tree is a hierarchical granularity tree. In information systems, each condition attribute divides the domain into several parts of a granular space. In the granular space, the classification preference ratio is used to describe the condition attribute. The classification preference ratio of every condition attribute is computed, and then the maximum attribute is chosen to divide the domain. According to different values of the attribute, the sample set is divided into several subsets. Each subset is a node or branch of the decision tree. If all the objects in a node are the same class, this node is a leaf node without further division. Otherwise, the node is not a consistent node. Above process of division will be repeated for all inconsistent nodes until all nodes become leaf nodes. Now the decision tree is finished. An example is given, which shows that the algorithm is feasible.

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/).

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TY  - CONF
AU  - Jing Lin
PY  - 2015/04
DA  - 2015/04
TI  - The Study On A Decision Tree Based On The Classification Preference Ratio
BT  - Proceedings of the 2015 International Conference on Automation, Mechanical Control and Computational Engineering
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/amcce-15.2015.298
DO  - 10.2991/amcce-15.2015.298
ID  - Lin2015/04
ER  -