Associative classification using an immune optimization algorithm
- Resource Type
- Conference
- Authors
- Zhang, Lei; Meng, lingrui; Hou, chunjie
- Source
- 2012 IEEE International Conference on Automation and Logistics Automation and Logistics (ICAL), 2012 IEEE International Conference on. :179-184 Aug, 2012
- Subject
- Robotics and Control Systems
Signal Processing and Analysis
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Association rules
Classification algorithms
Sociology
Statistics
Training
Accuracy
Cloning
classification
association rules
associative classification
immune optimization
- Language
- ISSN
- 2161-8151
2161-816X
Associative classification algorithms which are based on association rules have performed well compared with other classification approaches. However a fundamental limitation with these classification algorithms is that the search space of candidate rules is very large and the processes of rule discovery and rule selection are conducted separately. This paper proposes an algorithm based on immune optimization mechanism for optimizing associative classification rules. In the proposed algorithm the rule search process and the rule selection process are integrated in a more reasonable way in the optimization process of associative rules, thus it has the capability of dealing with complex search space of association rules while still ensuring that the resultant set of association rules is appropriate for associative classification. The performance evaluation results have shown that the proposed algorithm has achieved good runtime and accuracy performance for categorical and text datasets in comparison with conventional associative classification algorithms.