Physical Sciences and Mathematics Commons^™

A Comparative Study Of Filter-Based Feature Ranking Techniques, Huanjing Wang, Taghi M. Khoshgoftaar, Kehan Gao

Dr. Huanjing Wang

One factor that affects the success of machine learning is the presence of irrelevant or redundant information in the training data set. Filter-based feature ranking techniques (rankers) rank the features according to their relevance to the target attribute and we choose the most relevant features to build classification models subsequently. In order to evaluate the effectiveness of different feature ranking techniques, a commonly used method is to assess the classification performance of models built with the respective selected feature subsets in terms of a given performance metric (e.g., classification accuracy or misclassification rate). Since a given performance metric usually can …