Artificial Immune System for Associative Classification

Do, T. D., Hui, S. C. and Fong, A. C. M. (2005) Artificial Immune System for Associative Classification. In: First International Conference, ICNC 2005, Changsha, China, 27-29 Aug 2005, pp. 849-858. (doi: 10.1007/11539117_119)

Abstract

Artificial Immune Systems (AIS), which are inspired from nature immune system, have recently been investigated for many information processing applications, such as feature extraction, pattern recognition, machine learning and data mining. In this paper, we investigate AIS, and in particular the clonal selection algorithm for Associative Classification (AC). To implement associative classification effectively, we need to tackle the problems on the very large search space of candidate rules during the rule mining process. This paper proposes a new approach known as AIS-AC for mining association rules effectively for classification. In AIS-AC, we treat the rule mining process as an optimization problem of finding an optimal set of association rules according to some predefined constraints. The proposed AIS-AC approach is efficient in dealing with the complexity problem on the large search space of rules. It avoids searching greedily for all possible association rules, and is able to find an effective set of associative rules for classification.

• Item Type: Conference Proceedings
• Additional Information: Online ISBN: 9783540318583
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Fong, Dr Alvis Cheuk Min
• Authors: Do, T. D., Hui, S. C., and Fong, A. C. M.
• College/School: College of Science and Engineering > School of Computing Science
• ISSN: 0302-9743