Tuning a Multiple Classifier System for Side Effect Discovery using Genetic Algorithms
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.
Cite
@article{arxiv.1409.1053,
title = {Tuning a Multiple Classifier System for Side Effect Discovery using Genetic Algorithms},
author = {Jenna M. Reps and Uwe Aickelin and Jonathan M. Garibaldi},
journal= {arXiv preprint arXiv:1409.1053},
year = {2014}
}
Comments
Proceedings of the 2014 World Congress on Computational Intelligence (WCCI 2014), pp. 910-917, IEEE, Beijing, 2014