On Selection of Optimal Classifiers

Abstract

The current advances of computational power and storage allow more models to be created and stored from significant data resources. This progress opens the opportunity to re-cycle and re-use such models in similar exercises. The evaluation of the machine learning algorithms and selection of an appropriate classifier from an existing collection of classifiers are still challenging tasks. In most cases, the decision of selecting the classifier is left to the user. When the selection is not performed accurately, the outcomes can have unexpected performance results. Classification algorithms aim to optimise some of the distinct objectives such as minimising misclassification error, maximising the accuracy, or maximising the model quality. The right choice for each of these objectives is critical to the quality of the classifier selected. This work aims to study the use of a multi-objective method that can be undertaken to find a set of suitable classifiers for a problem at hand. In this study, we applied seven classifiers on mental health data sets for classifier selection in terms of correctness and reliability. The experimental results suggest that this approach is useful in finding the best trade-off among the objectives of selecting a suitable classifier framework.