prikaz prve stranice dokumenta Effects of dataset characteristics on the performance of feature selection techniques
No public access
Scientific paper - Original scientific paper
Effects of dataset characteristics on the performance of feature selection techniques
Applied Soft Computing, 52 (2017); 109-119. https://doi.org/10.1016/j.asoc.2016.12.023
Oreški, Dijana; Oreški, Stjepan; Kliček, Božidar

Cite this document

Oreški, D., Oreški, S. & Kliček, B. (2017). Effects of dataset characteristics on the performance of feature selection techniques. Applied Soft Computing, 52., 109-119. doi: 10.1016/j.asoc.2016.12.023

Oreški, Dijana, et al. "Effects of dataset characteristics on the performance of feature selection techniques." Applied Soft Computing, vol. 52, 2017, pp. 109-119. https://doi.org/10.1016/j.asoc.2016.12.023

Oreški, Dijana, Stjepan Oreški and Božidar Kliček. "Effects of dataset characteristics on the performance of feature selection techniques." Applied Soft Computing 52 (2017): 109-119. https://doi.org/10.1016/j.asoc.2016.12.023

Oreški, D., Oreški, S. and Kliček, B. (2017) 'Effects of dataset characteristics on the performance of feature selection techniques', Applied Soft Computing, 52, pp. 109-119. doi: 10.1016/j.asoc.2016.12.023

Oreški D, Oreški S, Kliček B. Effects of dataset characteristics on the performance of feature selection techniques. Applied Soft Computing [Internet]. 2017 March [cited 2024 December 01];52:109-119. doi: 10.1016/j.asoc.2016.12.023

D. Oreški, S. Oreški and B. Kliček, "Effects of dataset characteristics on the performance of feature selection techniques", Applied Soft Computing, vol. 52, pp. 109-119, March 2017. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:211:165896. [Accessed: 01 December 2024]

Please login to the repository to save this object to your list.
Metadata
Title (english)Effects of dataset characteristics on the performance of feature selection techniques
AuthorDijana Oreški
AuthorStjepan Oreški
AuthorBožidar Kliček
Author's institutionUniversity of Zagreb
Faculty of Organization and Informatics
Scientific / art field,
discipline and subdiscipline		SOCIAL SCIENCES
Information and Communication Sciences
Abstract (english)
While extensive research in data mining has been devoted to developing better feature selection techniques, none of this research has examined the intrinsic relationship between dataset characteristics and a feature selection techniques performance. Thus, our research examines experimentally how dataset characteristics affect both the accuracy and the time complexity of feature selection. To evaluate the performance of various feature selection techniques on datasets of different characteristics, extensive experiments with five feature selection techniques, three types of classification algorithms, seven types of dataset characterization methods and all possible combinations of dataset characteristics are conducted on 128 publicly available datasets. We apply the decision tree method to evaluate the interdependencies between dataset characteristics and performance. The results of the study reveal the intrinsic relationship between dataset characteristics and feature selection techniques performance. Additionally, our study contributes to research in data mining by providing a roadmap for future research on feature selection and a significantly wider framework for comparative analysis. (C) 2016 Elsevier B.V. All rights reserved.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleApplied Soft Computing
Numberingvol. 52, pp. 109-119
p-ISSN1568-4946
e-ISSN1872-9681
DOIhttps://doi.org/10.1016/j.asoc.2016.12.023
URN:NBNurn:nbn:hr:211:165896
Publication2017-03
Document URLhttp://www.sciencedirect.com/science/article/pii/S156849461630641X
Type of resourceText
Access conditionsClosed access
Terms of useLicence In copyright icon
Created on2017-09-19 07:44:59