Feature Selection and Modeling using Statistical and Machine learning Methods

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

doi:10.1109/DISCOVER50404.2020.9278093

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 proceeding › Conference contribution

2 Citations (Scopus)

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 language	English
Title of host publication	2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	18-22
Number of pages	5
ISBN (Electronic)	9781728198859
DOIs	https://doi.org/10.1109/DISCOVER50404.2020.9278093
Publication status	Published - 30-10-2020
Event	2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 - Udupi, India Duration: 30-10-2020 → 31-10-2020

Publication series

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

Conference

Conference	2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020
Country/Territory	India
City	Udupi
Period	30-10-20 → 31-10-20

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

Access to Document

10.1109/DISCOVER50404.2020.9278093

Cite this

D'souza, S., Prema, K. V., & Balaji, S. (2020). Feature Selection and Modeling using Statistical and Machine learning Methods. In 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 - Proceedings (pp. 18-22). [9278093] (2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DISCOVER50404.2020.9278093

D'souza, Sofia ; Prema, K. V. ; Balaji, S. / Feature Selection and Modeling using Statistical and Machine learning Methods. 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 18-22 (2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 - Proceedings).

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title = "Feature Selection and Modeling using Statistical and Machine learning Methods",

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.",

author = "Sofia D'souza and Prema, {K. V.} and S. Balaji",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 ; Conference date: 30-10-2020 Through 31-10-2020",

year = "2020",

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language = "English",

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

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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D'souza, S, Prema, KV & Balaji, S 2020, Feature Selection and Modeling using Statistical and Machine learning Methods. in 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 - Proceedings., 9278093, 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 18-22, 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020, Udupi, India, 30-10-20. https://doi.org/10.1109/DISCOVER50404.2020.9278093

Feature Selection and Modeling using Statistical and Machine learning Methods. / D'souza, Sofia; Prema, K. V.; Balaji, S.
2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. p. 18-22 9278093 (2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Prema, K. V.

AU - Balaji, S.

PY - 2020/10/30

Y1 - 2020/10/30

N2 - 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.

AB - 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.

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M3 - Conference contribution

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

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D'souza S, Prema KV , Balaji S. Feature Selection and Modeling using Statistical and Machine learning Methods. In 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. p. 18-22. 9278093. (2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2020 - Proceedings). doi: 10.1109/DISCOVER50404.2020.9278093

Feature Selection and Modeling using Statistical and Machine learning Methods

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