Tuning a Multiple Classifier System for Side Effect Discovery using Genetic Algorithms
This work addresses side effect detection in a specific domain, presenting an incremental improvement over previous methods.
The paper tackled the problem of side effect discovery by tuning a multiple classifier system with genetic algorithms, achieving a higher partial area under the ROC curve compared to a single classifier and maintaining a low false positive rate.
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.