Feature Selection and Modeling using Statistical and Machine learning Methods

Sofia D'souza, K. V. Prema, S. Balaji

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Feature selection is a necessary step in machine learning regression problems that aims to find relevant and reduced set of features. In this research, we assessed the performance of three different learning models on a Quantitative structure activity relationship (QSAR) dataset. Learning models were developed from a pool of features selected by three different variable selection techniques. The results indicate that the final learning models built using statistically significant features exhibit improved predictive performance. Further, Partial least squares (PLS) learning model has shown better predictive performance compared to other learning models on the external test set.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages18-22
Number of pages5
ISBN (Electronic)9781728198859
DOIs
Publication statusPublished - 30-10-2020
Event2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 - Udupi, India
Duration: 30-10-202031-10-2020

Publication series

Name2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 - Proceedings

Conference

Conference2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020
CountryIndia
CityUdupi
Period30-10-2031-10-20

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Control and Optimization
  • Artificial Intelligence
  • Computer Networks and Communications

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