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.
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Do, T.D., Hui, S.C., Fong, A.C.M. (2005). Artificial Immune System for Associative Classification. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_119
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DOI: https://doi.org/10.1007/11539117_119
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