The Impact of Enhanced Space Forests with Homogeneous Classifier Ensembles
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In this paper, we propose to advance the classification success of classifier ensembles by investigating the contribution of enhanced space forests. For this purpose, this study especially is focused on enhanced feature spaces by implementing the most popular feature selection techniques, namely information gain, and chi-square. After performing these methods on the original feature space, training phase is evaluated with all the original and the modified versions of most significant features, which are acquired by applying difference operator to the original features and the selected features with feature selection methods. That is, the new training dataset is constructed by combining the original features and the new ones. Then, the training is done with the well-known classification algorithm namely, decision tree using the enhanced feature space. Finally, three types of ensemble algorithms namely, bagging, random subspace, and random forest are carried out. A wide range of comparative experiments are conducted on publicly available and widely-used 36 datasets from the UCI machine learning repository to observe the impact of the enhanced space forests with classifier ensembles. Experiment results demonstrate that the proposed enhanced space forests perform better classification accuracy than the state of the art studies. Approximately, 1% - 3% improvement of the classification success is an indicator that our proposed technique is efficient.