Structured Ranking Method-based Feature Selection in Data Mining

Abstract

Feature selection has been emphasized on an operative approach for dealing with large volume data. The majority of these approaches are skewed into high-ranking features to get well right features towards classification. This paper proposes a structured feature ranking (SFR) approach for large volume data to address this challenge. We present a subspace feature-based clustering approach to find out feature-based cluster as per class labels. The various feature clusters are created ranked for features independently using the SFR approach, based on the subspace weight provided by SFC. Then, for ranking the features, we offer a structured feature weighting method in which the high-rank characteristics are utilized for class labels. SFC's approach has been tested in a variety of features. On a collection of large volume datasets, the proposed SFR approach is compared to six feature selection methods. The results demonstrate that SFR method outperformed than methods.