A relief-TOPSIS based feature selection for high-dimensional data

Since their emergence, high dimensional data have imposed a big challenge to researchers given their complexities. To deal with this problem, using feature selection is the best solution, it plays a very important role in high dimensional data analysis. This process will first allow us to extract the relevant features, remove redundancy and noise, decrease the computation time and improve the classification performances. In the literature, there are several methods for feature selection, of which three categories can be distinguished. In this paper, we will focus on the filter methods for feature selection, this category estimates the quality of the variables individually independently of any learning algorithm based on statistical measures. Among the most popular filters, there is the Relief method created by Kira and Rendell as an instance-based learning-inspired idea, it gives weight to each feature and selects the highest-scored features. In this study, we propose a filter method for feature selection called ReliefTP. ReliefTP is a combination of the Relief concepts and the Technique for Order Preference by Similarity to the Ideal Solution(TOPSIS). Several experiments were carried out on four biomedical datasets the most famous including a comparison with the original Relief. The classification results achieved by the different classification methods confirm the effectiveness of our proposed method.