Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/29619
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Mostert, Werner
Malan, Katherine M
Ochoa, Gabriela
Engelbrecht, Andries P
Contact Email: gabriela.ochoa@cs.stir.ac.uk
Title: Insights into the feature selection problem using local optima networks
Editor(s): Liefooghe, A
Paquete, L
Citation: Mostert W, Malan KM, Ochoa G & Engelbrecht AP (2019) Insights into the feature selection problem using local optima networks. In: Liefooghe A & Paquete L (eds.) Evolutionary Computation in Combinatorial Optimization. Lecture Notes in Computer Science, 11452. 19th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2019, held as part of EvoStar 2019, Leipzig, Germany, 24.04.2019-26.04.2019. Cham, Switzerland: Springer Verlag, pp. 147-162. https://doi.org/10.1007/978-3-030-16711-0_10
Issue Date: 2019
Series/Report no.: Lecture Notes in Computer Science, 11452
Conference Name: 19th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2019, held as part of EvoStar 2019
Conference Dates: 2019-04-24 - 2019-04-26
Conference Location: Leipzig, Germany
Abstract: The binary feature selection problem is investigated in this paper. Feature selection fitness landscape analysis is done, which allows for a better understanding of the behaviour of feature selection algorithms. Local optima networks are employed as a tool to visualise and characterise the fitness landscapes of the feature selection problem in the context of classification. An analysis of the fitness landscape global structure is provided, based on seven real-world datasets with up to 17 features. Formation of neutral global optima plateaus are shown to indicate the existence of irrelevant features in the datasets. Removal of irrelevant features resulted in a reduction of neutrality and the ratio of local optima to the size of the search space, resulting in improved performance of genetic algorithm search in finding the global optimum.
Status: AM - Accepted Manuscript
Rights: This is a post-peer-review, pre-copyedit version of an article published in Liefooghe A & Paquete L (eds.) Evolutionary Computation in Combinatorial Optimization. Lecture Notes in Computer Science, 11452. 19th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2019, held as part of EvoStar 2019, Leipzig, Germany, 24.04.2019-26.04.2019. Cham, Switzerland: Springer Verlag, pp. 147-162. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-16711-0_10

Files in This Item:
File Description SizeFormat 
FeatureSelectionLONEvoCOP2019.pdfFulltext - Accepted Version7.12 MBAdobe PDFView/Open



This item is protected by original copyright



Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.