Applications of Feature Selection and Regression Techniques in Materials Design: A Tutorial

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Author(s): Partha Dey (Academy of Technology, Adisaptagram, Hooghly, India), Joe Bible (Eunice Kennedy Shriver National Institute of Child Health and Human Development, USA), Swati Dey (Indian Institute of Engineering, Science and Technology Shibpur, India)and Somnath Datta (University of Florida, USA)
Copyright: 2016
Pages: 28
Source title: Computational Approaches to Materials Design: Theoretical and Practical Aspects
Source Author(s)/Editor(s): Shubhabrata Datta (Calcutta Institute of Engineering and Management, India)and J. Paulo Davim (University of Aveiro, Portugal)
DOI: 10.4018/978-1-5225-0290-6.ch008

Keywords: Engineering Science Reference / Materials Science / Materials Science and Engineering / Science & Engineering

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Abstract

Feature selection is considered as an important preprocessing step to data mining and soft computing, whereas regression is a collection of methods to optimally assess the signal from a noisy output. Both seek to arrive at the dependence and relation between different attributes and a target material property. In the present chapter a flock of regression and feature selection techniques are discussed, and the kind of results that can be obtained with each of them has been illustrated with the help of a dataset on steel. The different methods are capable of abstracting data in different forms, thus revealing hidden knowledge from different perspectives. Choosing the most appropriate method depends on the application at hand and the kind of objective that one is looking for.

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