A Contrast Based Feature Selection Algorithm for High-dimensional Data set in Machine Learning
This addresses the bottleneck of high computation costs in feature selection for machine learning practitioners, though it appears incremental as it builds on existing filter methods.
The paper tackles the high computational cost of feature selection on large datasets by proposing ContrastFS, a filter method that selects discriminative features based on class discrepancies, achieving favorable performance with negligible computation compared to state-of-the-art methods.
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.