Conferences in Research and Practice in Information Technology
  

Online Version - Last Updated - 20 Jan 2012

 

 
Home
 

 
Procedures and Resources for Authors

 
Information and Resources for Volume Editors
 

 
Orders and Subscriptions
 

 
Published Articles

 
Upcoming Volumes
 

 
Contact Us
 

 
Useful External Links
 

 
CRPIT Site Search
 
    

Associative Classi cation using a Bio-Inspired Algorithm

Soliman, O. S., Bahgat, R. and Adly, A.

    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.
Cite as: Soliman, O. S., Bahgat, R. and Adly, A. (2012). Associative Classi cation using a Bio-Inspired Algorithm. In Proc. Data Mining and Analytics 2012 (AusDM 2012) Sydney, Australia. CRPIT, 134. Zhao, Y., Li, J. , Kennedy, P.J. and Christen, P. Eds., ACS. 119 - 126
pdf (from crpit.com) pdf (local if available) BibTeX EndNote GS