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.
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.
Actions (Archive Staff Only)