Tuning a multiple classifier system for side effect discovery using genetic algorithms

Reps, Jenna M.; Aickelin, Uwe; Garibaldi, Jonathan M.

Abstract

In previous work, a novel supervised framework implementing a binary classifier was presented that obtained excellent results for side effect discovery. Interestingly, unique side effects were identified when different binary classifiers were used within the framework, prompting the investigation of applying a multiple classifier system. In this paper we investigate tuning a side effect multiple classifying system using genetic algorithms. The results of this research show that the novel framework implementing a multiple classifying system trained using genetic algorithms can obtain a higher partial area under the receiver operating characteristic curve than implementing a single classifier. Furthermore, the framework is able to detect side effects efficiently and obtains a low false positive rate.

Citation

Reps, J. M., Aickelin, U., & Garibaldi, J. M. (2014, July). Tuning a multiple classifier system for side effect discovery using genetic algorithms. Presented at Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014, Beijing, China

• Presentation Conference Type: Edited Proceedings
• Conference Name: Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
• Start Date: Jul 6, 2014
• End Date: Jul 11, 2014
• Acceptance Date: Jul 1, 2014
• Online Publication Date: Sep 22, 2014
• Publication Date: Jul 6, 2014
• Deposit Date: Sep 30, 2014
• Publicly Available Date: Sep 30, 2014
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 910-917
• Book Title: 2014 IEEE Congress on Evolutionary Computation (CEC)
• ISBN: 9781479914883
• DOI: https://doi.org/10.1109/CEC.2014.6900328
• Keywords: adr, Biomedical Informatics, bradford hill, ensemble
• Public URL: https://nottingham-repository.worktribe.com/output/995448
• Publisher URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6900328
• Additional Information: Published in: 2014 IEEE Congress on Evolutionary Computation (CEC). Piscataway, NJ : IEEE, 2014 (ISBN: 9781479966264). pp. 910-917 (doi: 10.1109/CEC.2014.6900328). © 2014 IEEE
• Contract Date: Sep 30, 2014