A Contrast Based Feature Selection Algorithm for High-dimensional Data
  set in Machine Learning
Chunxu Cao Qiang Zhang
Feature selection is an important process in machine learning and knowledge
discovery. By selecting the most informative features and eliminating
irrelevant ones, the performance of learning algorithms can be improved and the
extraction of meaningful patterns and insights from data can be facilitated.
However, most existing feature selection methods, when applied to large
datasets, encountered the bottleneck of high computation costs. To address this
problem, we propose a novel filter feature selection method, ContrastFS, which
selects discriminative features based on the discrepancies features shown
between different classes. We introduce a dimensionless quantity as a surrogate
representation to summarize the distributional individuality of certain
classes, based on this quantity we evaluate features and study the correlation
among them. We validate effectiveness and efficiency of our approach on several
widely studied benchmark datasets, results show that the new method performs
favorably with negligible computation in comparison with other state-of-the-art
feature selection methods.