A Heuristic Method for Deriving Range-Based Classification Rules

摘要：

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.

作者: Achilleas Tziatzios Jianhua Shao Grigorios Loukides

作者单位: School of Computer Science and Informatics Cardiff University, Cardiff, UK Department of Biomedical Informatics Vanderbilt University, USA

会议类型: 国际会议

会议名称: 2011 Eighth International Conference on Fuzzy System and Knowledge Discovery(第八届模糊系统与知识发现国际会议 FSKD 2011)

会议地点: 上海

会议语种:英文

页码: 956-960

在线出版日期: 2011-07-26（万方平台首次上网日期，不代表论文的发表时间）