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Application of feature subset selection methods on classifiers comprehensibility for bio-medical datasets

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conference contribution
posted on 2023-05-23, 12:12 authored by Ali, SI, Byeong KangByeong Kang, Lee, S
Feature subset selection is an important data reduction technique. Effects of feature selection on classifier’s accuracy are extensively studied yet comprehensibility of the resultant model is given less attention. We show that a weak feature selection method may significantly increase the complexity of a classification model. We also proposed an extendable feature selection methodology based on our preliminary results. Insights from the study can be used for developing clinical decision support systems.

History

Publication title

Lecture Notes in Computer Science 8867: Proceedings of the 10th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2016)

Editors

CR Garcia, P Caballero-Gil, M Burmester, A Quesada-Arencibia

Pagination

38-43

ISBN

978-3-319-48745-8

Department/School

School of Information and Communication Technology

Publisher

Springer

Place of publication

Netherlands

Event title

10th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2016)

Event Venue

Canary Islands, Spain

Date of Event (Start Date)

2016-11-29

Date of Event (End Date)

2016-12-02

Rights statement

Copyright 2016 Springer International Publishing AG. This is an author-created version of a paper originally published in García C., Caballero-Gil P., Burmester M., Quesada-Arencibia A. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2016. Lecture Notes in Computer Science, vol 10069. Springer, Cham. The final publication is available at Springer via https://doi.org/10.1007/978-3-319-48746-5_4

Repository Status

  • Open

Socio-economic Objectives

Information systems, technologies and services not elsewhere classified

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