118089 - application of feature subset selection methods.pdf (538.23 kB)
Application of feature subset selection methods on classifiers comprehensibility for bio-medical datasets
conference contribution
posted on 2023-05-23, 12:12 authored by Ali, SI, Byeong KangByeong Kang, Lee, SFeature 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-ArencibiaPagination
38-43ISBN
978-3-319-48745-8Department/School
School of Information and Communication TechnologyPublisher
SpringerPlace of publication
NetherlandsEvent title
10th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2016)Event Venue
Canary Islands, SpainDate of Event (Start Date)
2016-11-29Date of Event (End Date)
2016-12-02Rights 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_4Repository Status
- Open