Dimensionality Reduction Approaches And Evolving Challenges In High Dimensional Data

Feature selection plays an important role in data mining and machine learning. It helps to reduce the dimensionality of data and increase the performance of classification algorithms. A variety of feature selection methods have been presented in state-of-the-art literature to resolve feature selection problems such as large search space in high dimensional datasets like in microarray. However, it is a challenging task to identify the best feature selection method that suits a specific scenario or situation. In this paper, we present a comprehensive survey of the recent research work on feature selection methods, their types, strengths and weaknesses, and recent contributions in related areas. Current issues and challenges are also discussed to identify future research directions.