Analysis of Rank Aggregation Techniques for Rank Based on the Feature Selection Technique

In order to improve classification accuracy and lower future computation data collecting costs, feature selection is the process of choosing most crucial features from a group attributes removing less or redundant ones. To narrow down that need be analyzed, variety procedures have been detailed in published publications. Chi-Square (CS), IG, Relief, GR, Symmetrical Uncertainty (SU), MI are six alternative methods used this study. The provided dataset aggregated using four rank aggregation strategies: "rank aggregation," "Borda Count (BC) methodology," "score combination," "unified scoring" based on outcomes method (UFS). These by themselves were unable generate clear for characteristic. produce different ranks traits, ensemble aggregating carried out. For this, bagging majority voting was applied.