An Artificial Immune System Approach to Associative Classification [chapter]

Samir A. Mohamed Elsayed, Sanguthevar Rajasekaran, Reda A. Ammar
2012 Lecture Notes in Computer Science  
Associative Classification (AC) mining is a promising approach that uses association rules discovery techniques to construct association classifiers. However, traditional AC algorithms typically search for all possible association rules to find a representative subset of those rules. Since the search space of such rules may grow exponentially as the support threshold decreases, the rules discovery process can be computationally expensive. One effective way to tackle this problem is to directly
more » ... ind a set of high-stakes association rules that potentially builds a highly accurate classifier. This paper introduces AC-CS, a novel AC algorithm, inspired by the clonal selection of the immune system. The algorithm proceeds in an evolutionary fashion to populate only rules that are likely to yield good classification accuracy. Empirical results on real datasets of different sizes show that the approach generates dramatically less rules than traditional AC algorithms. Hence, the proposed approach is indeed significantly more efficient than traditional AC algorithms while achieving good competitive accuracy.
doi:10.1007/978-3-642-31125-3_13 fatcat:kg32k5c3h5bndeyauvjelhb3pe