A Rule Induction with Hierarchical Decision Attributes

Chun-Che Huang, Shian-Hua Lin, Zhi-Xing Chen, You-Ping Wang

Abstract

Hierarchical attributes are usually predefined in real-world applications and can be represented by a concept hierarchy, which is a kind of concise and general form of concept description that organizes relationships of data. To induct rules from the qualitative and hierarchical nature in data, the rough set approach is one of the promised solutions in data mining. However, previous rough set approaches induct decision rules that contain the decision attribute in the same hierarchical level. In addition, comparison of the reducts using the Strength Index (SI), which is introduced to identify meaningful reducts, is limited to same number of attributes. In this paper, a hierarchical rough set (HRS) problem is defined and the solution approach is proposed. The proposed solution approach is expected to increase potential benefits in decision making.

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Paper Citation


in Harvard Style

Huang C., Lin S., Chen Z. and Wang Y. (2013). A Rule Induction with Hierarchical Decision Attributes . In Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8565-59-4, pages 95-102. DOI: 10.5220/0004410900950102


in Bibtex Style

@conference{iceis13,
author={Chun-Che Huang and Shian-Hua Lin and Zhi-Xing Chen and You-Ping Wang},
title={A Rule Induction with Hierarchical Decision Attributes},
booktitle={Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2013},
pages={95-102},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004410900950102},
isbn={978-989-8565-59-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - A Rule Induction with Hierarchical Decision Attributes
SN - 978-989-8565-59-4
AU - Huang C.
AU - Lin S.
AU - Chen Z.
AU - Wang Y.
PY - 2013
SP - 95
EP - 102
DO - 10.5220/0004410900950102