The evaluation of heterogeneous classifier ensembles for Turkish texts

Abstract

The basic idea behind the classifier ensembles is to use more than one classifier by expecting to improve the overall accuracy. It is known that the classifier ensembles boost the overall classification performance by depending on two factors namely, individual success of the base learners and diversity. One way of providing diversity is to use the same or different type of base learners. When the same type of base learners is used, the diversity is realized by using different training data subsets for each of base classifiers. When different type of base classifiers used to achieve diversity, then ensemble system is called heterogeneous. In this paper, we focus on the heterogonous ensembles that use different types of base learners. An ensemble system based on classification algorithms, naïve Bayes, support vector machine and random forest is used to measure the effectiveness of heterogeneous classifier ensembles by conducting experiments on Turkish texts. Experiment results demonstrate that the usage of heterogeneous ensembles improves classification performance for Turkish texts and encourages to evaluate the impact of heterogeneous ensembles for the other agglutinative languages.