Fundamentals and Methods of Machine and Deep Learning

5The Significance of Feature Selection Techniques in Machine Learning

N. Bharathi¹, B.S. Rishiikeshwer², T. Aswin Shriram²,

B. Santhi²* and G.R. Brindha^2†

¹Department of CSE, SRM Institute of Science and Technology, Vadapalani, Chennai, Tamil Nadu, India

²School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, India

Abstract

Current digital era with tons of raw data and extracting insight from this is a significant process. Initial significant step is to pre-process the available data set. The pre-processed input is to be fed to the proper Machine Learning (ML) model to extract the insight or decision. The performance of the model purely depends on the features given to the model. Without the knowledge of feature selection process, perfect model building will be a question mark. Proper selection of feature is essential for building precise model. Plethora of techniques are available in the literature for feature extraction and feature selection. Irrelevant features may drastically decrease the performance of the model and increase the complexity. Though features are describing the record in an effective way, by representing the record with lesser number of features through optimal approach for predicting unseen record precisely is a complex task. To handle such complexities, appropriate feature selection methods are used. Hence, this chapter concentrates on different feature selection techniques with its merits and limitations. The discussion is supported with ...