Unsupervised feature selection methods can be more efficient than supervised methods, which rely on the expensive and time-consuming 3 Piece Power Reclining Sectional with Chaise data labeling process.The paper introduced skewness as a novel, unsupervised, and computationally efficient feature ranking metric, suitable for both classification and regression tasks.Its feature selection effectiveness is compared to several state-of-the-art supervised and unsupervised feature ranking and selection methods.
Both theoretical analysis and empirical evaluation on several popular classification and regression algorithms show that statistical moment-based feature selection algorithms are competitive in terms of accuracy and mean squared error (MSE) with the state-of-the-art supervised approaches for feature ranking and selection, including Fast Correlation Based Filter (FCBF), Minimum Redundancy Toy Cookware Maximum Relevance (MRMR), and Mutual Information Maximization (MIM).We also present a mathematical proof based on some common assumptions, which explains the high effectiveness of statistical moments in the feature ranking procedure.Moreover, statistical moment-based feature selection is shown empirically to run faster, on average, than the supervised approaches and the unsupervised Laplacian Score method.
Additionally, skewness-based feature selection, in contrast to variance-based selection, does not depend on data normalization that requires additional computational time and may affect the feature ranking results.