Unsupervised Feature Selection Using Correlation Score

Data of huge dimensionality is generated because of wide application of technologies. Using this data for the very purpose of decision-making is greatly affected because of the curse of dimensionality as selection of all features will lead to overfitting and ignoring the relevant ones can lead to information loss. Feature selection algorithms help to overcome this problem by identifying the subset of original features by retaining relevant features and by removing the redundant ones. This paper aims to evaluate and analyze some of the most popular feature selection algorithms using different benchmarked datasets. Relief, ReliefF, and Random Forest algorithms are evaluated and analyzed in the form of combinations of different rankers and classifiers. It is observed empirically that the accuracy of the ranker and classifier varies from dataset to dataset. This paper introduces the concept of applying multivariate correlation analysis (MCA) for feature selection. From results, it can be inferred that MCA exhibits better performance over the legacy-based feature selection algorithms.