Abstract
Prediction plays a significant role in the human life to predict the situation, climate, finance, outcome of the particular event or activities, etc. This predication can be achieved by the classifier which is formally known as supervised learner. The classifier can be built using the dataset and its performance is based on the attributes or features present in the dataset which are highly relevant to the predictive target attributes. The feature selection process removes the redundant and irrelevant features from the dataset to improve the performance of the classifier. This paper proposes a rough set-based feature selection method to remove the redundant and irrelevant features in order to improve the performance the classifier. The proposed method is tested on the various datasets with the various supervised learning algorithms and it is evident that the proposed method producing the better performance than the other methods compared.
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