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How to accurately measure the relevance and redundancy of features is anage-old challenge in the field of feature selection. However, existingfilter-based feature selection methods cannot directly measure redundancy forcontinuous data. In addition, most methods rely on manually specifying thenumber of features, which may introduce errors in the absence of expertknowledge. In this paper, we propose a non-parametric feature selectionalgorithm based on maximum inter-class variation and minimum redundancy,abbreviated as MVMR-FS. We first introduce supervised and unsupervised kerneldensity estimation on the features to capture their similarities anddifferences in inter-class and overall distributions. Subsequently, we presentthe criteria for maximum inter-class variation and minimum redundancy (MVMR),wherein the inter-class probability distributions are employed to reflectfeature relevance and the distances between overall probability distributionsare used to quantify redundancy. Finally, we employ an AGA to search for thefeature subset that minimizes the MVMR. Compared with ten state-of-the-artmethods, MVMR-FS achieves the highest average accuracy and improves theaccuracy by 5% to 11%.

MVMR-FS : Non-parametric feature selection algorithm based on Maximum inter-class Variation and Minimum Redundancy