Examining the Success of Information Gain, Pearson Correlation, and Symmetric Uncertainty Ranking Methods on 3D Hand Posture Data for Metaverse Systems

Abstract

Metaverse is a hardware and software interface space that can connect people's social lives as in the real-natural world and provide the feeling of being there at the maximum level. In order for metaverse systems to be efficient, many independent accessories have to work holistically. One of these accessories is wearable gloves called meta gloves and equipped with sensors. Thanks to it, an important stage of metaverse systems is completed with the detection of 3-dimensional (3D) hand postures. In this study, the success of Information Gain, Pearson’s Correlation, and Symmetric Uncertainty ranking methods on 3D hand posture data for metaverse systems were investigated. For this purpose, various preprocessing was performed on the 3D data, and a dataset consisting of 15 features in total was created. The created dataset was ranked by 3 different methods mentioned and the features that the methods determined effectively were classified separately. Obtained results were interpreted with various statistical evaluation criteria. According to the experimental results obtained, it has been seen that the Symmetric Uncertainty ranking algorithm produces successful results for metaverse systems. As a result of the classification made with the active features determined using this method, there has been an increase in statistical performance criteria compared to other methods. In addition, it has been proven that time loss can be avoided in the classification of big data similar to the data used.