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

Reps, Jenna M. and Aickelin, Uwe and Garibaldi, Jonathan M. (2014) Tuning a multiple classifier system for side effect discovery using genetic algorithms. In: 2014 IEEE Congress on Evolutionary Computation (CEC), 6-11 July 2014, Beijing, China.

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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.

Item Type: Conference or Workshop Item (Paper)
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
Keywords: adr, Biomedical Informatics, bradford hill, ensemble
Schools/Departments: University of Nottingham UK Campus > Faculty of Science > School of Computer Science
Depositing User: Aickelin, Professor Uwe
Date Deposited: 30 Sep 2014 11:44
Last Modified: 20 Sep 2016 17:03
URI: http://eprints.nottingham.ac.uk/id/eprint/3354

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