01657nas a2200217   4500000000100000000000100001008004100002260001200043653002100055653002200076653002400098653003600122100001500158700001600173245007600189856010000265300001100365490000600376520104300382022001401425       2020                            d  c03/202010aMachine Learning10aFeature Selection10aK-Nearest Neighbors10aBinary Multi-Verse Optimization1 aRahul Hans1 aHarjot Kaur00aBinary Multi-Verse Optimization (BMVO) Approaches for Feature Selection  uhttps://www.ijimai.org/journal/sites/default/files/files/2019/07/ijimai20206_1_11_pdf_31520.pdf  a91-1060 v63 aMulti-Verse Optimization (MVO) is one of the newest meta-heuristic optimization algorithms which imitates the theory of Multi-Verse in Physics and resembles the interaction among the various universes. In problem domains like feature selection, the solutions are often constrained to the binary values viz. 0 and 1. With regard to this, in this paper, binary versions of MVO algorithm have been proposed with two prime aims: firstly, to remove redundant and irrelevant features from the dataset and secondly, to achieve better classification accuracy. The proposed binary versions use the concept of transformation functions for the mapping of a continuous version of the MVO algorithm to its binary versions. For carrying out the experiments, 21 diverse datasets have been used to compare the Binary MVO (BMVO) with some binary versions of existing metaheuristic algorithms. It has been observed that the proposed BMVO approaches have outperformed in terms of a number of features selected and the accuracy of the classification process.  a1989-1660