A heuristic method for deriving range-based classification rules

Tziatzios, Achilleas, Shao, Jianhua ORCID: https://orcid.org/0000-0001-8461-1471 and Loukides, Grigorios ORCID: https://orcid.org/0000-0003-0888-5061 2011. A heuristic method for deriving range-based classification rules. Presented at: 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Shanghai, China, 26-28 July 2011. Published in: Ding, Yongsheng, Li, Yongmin, Fan, Zhun, Li, Shaoyuan and Wang, Lipo eds. Proceedings: 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). , vol.2 Los Alamitos, CA: IEEE, pp. 925-929. 10.1109/FSKD.2011.6019723

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Abstract

The ability to learn classification rules from data is important and useful in a range of applications. While many methods to facilitate this task have been proposed, few can derive classification rules that involve ranges (numerical intervals). In this paper, we consider how range-based classification rules may be derived from numerical data and propose a new method inspired by classification association rule mining. This method searches for associated ranges in a similar way to how associated itemsets are searched in categorical attributes in association rule mining, but uses class values to guide the search, so that only those ranges that are relevant to the derivation of classification rules are found. Our preliminary experiments demonstrate the effectiveness of our method.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	IEEE
ISBN:	9781612841809
Last Modified:	21 Oct 2022 08:40
URI:	https://orca.cardiff.ac.uk/id/eprint/34067

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