Analysis on hybrid dominance-based rough set parameterization using private financial initiative unitary charges data

Abstract

This paper evaluates the capability of the hybrid parameter reduction approach in handling private financial initiative (PFI) unitary charges data to increase the classification performance. The objective of this study is to analyse the performance of the proposed hybrid parameter reduction approach in assisting the neural network classifier to classify complex data sets that might contain uncertain and inconsistent problems. The proposed hybrid parameter reduction approach consists of several methods that will be executed during the data analysis process. Slicing technique and dominance-based rough set approach (DRSA) are the two techniques that play important roles in the proposed parameter reduction process. In order, to analyse the performance of the proposed work, the PFI data that covers all regions in Malaysia is applied in the experimental works. Besides, several standard data sets have also been used to validate the obtained results. The results reveal that the hybrid approach has successfully assisted the classifier in the classification process.