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.