Associative classification using a bio-inspired algorithm

Authors:
Omar S. Soliman;Roba Bahgat;Amr Adly
Affiliations:
Cairo University, Orman, Giza, Egypt;Cairo University, Orman, Giza, Egypt;Cairo University, Orman, Giza, Egypt
Venue:
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
Year:
2012

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Abstract

This paper proposes an ambitious bio-inspired algorithm for associative classification (AC) based on Quantum-Inspired Artificial Immune system (QAIS) for building an efficient classifier by searching association rules to find the best subset of rules for all possible association rules. it integrates concepts of quantum computing (QC) and artificial immune system (AIS) as a bio natural inspired algorithm. It employees a mutation operator with a quantum-based rotation gate to control and maintain diversity, and guides the search process. The proposed QAIS is implemented and evaluated using benchmark datasets(Blake & Merz 1998) including Adult, Nursery, Iris and Breast-Cancer datasets. The obtained results are analysed and compared with experimental implementation results of AIS-AC algorithm (Do et al 2009). The experimental results showed that the proposed algorithm is preformed well with large search space and has higher accuracy, and maintained diversity.