Combining Fuzzy Rough Set with Salient Features for HRM Classification

In today´s economic transformation setting round the globe, there has been a growing interest in Human Resources Management (HRM) of corporations and their consequence on revenues of these corporations. Yet, there are some challenges and issues in deciding about the best people with talents and recommending them for rising in financial gain or promotion based on some features which are vital for the interests of the corporations. This paper presents a solution for Human Resource Talent Management (HRTM) problem via using data mining techniques. In this research work, effective feature selection methods are used, then, the classification task is conducted via Fuzzy Rough Nearest neighbors, Decision Tree and Naïve Bayes. Basically, the information gained by using combining filter feature selection techniques then using Fuzzy Rough Set theory and depending on the results, Fuzzy Rough Nearest Neighbors classifier has the highest classification accuracy rate (which was 98.1174%) among others.