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

Citation

Ali, SI and Kang, BH and Lee, S, Application of feature subset selection methods on classifiers comprehensibility for bio-medical datasets, Lecture Notes in Computer Science 8867: Proceedings of the 10th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2016), 29 November - 2 December 2016, Canary Islands, Spain, pp. 38-43. ISBN 978-3-319-48745-8 (2016) [Refereed Conference Paper]


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Copyright 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

DOI: doi:10.1007/978-3-319-48746-5_4

Abstract

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.

Item Details

Item Type:Refereed Conference Paper
Keywords:feature subset selection, model comprehensibility, data classification, data mining, clinical decision support system
Research Division:Information and Computing Sciences
Research Group:Library and Information Studies
Research Field:Health Informatics
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Computer Software and Services not elsewhere classified
UTAS Author:Kang, BH (Professor Byeong Kang)
ID Code:118089
Year Published:2016
Deposited By:Information and Communication Technology
Deposited On:2017-07-04
Last Modified:2018-03-18
Downloads:86 View Download Statistics

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