Abstract

Feature Selection is the process of selecting the momentous feature subset from the original ones. This technique is frequently used as a preprocessing technique in data mining. In this study, a new feature selection algorithm is proposed and is called Modified Fisher Score Principal Feature Analysis (MFSPFA). The new algorithm is developed by combining the proposed Modified Fisher Score (MFS) and Principal Feature Analysis (PFA). The proposed algorithm is tested on publicly available datasets. The experimental results show that, the proposed algorithm is able to reduce the futile features and improves the classification accuracy.

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