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Analysing the predictivity of features to characterise the search space

Durgut, Rafet; Aydin, Mehmet Emin; Ihshaish, Hisham; Rakib, Abdur

Authors

Rafet Durgut

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Dr Mehmet Aydin Mehmet.Aydin@uwe.ac.uk
Senior Lecturer in Networks and Mobile Computing

Hisham Ihshaish Hisham.Ihshaish@uwe.ac.uk
Senior Lecturer in Information Science

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Rakib Abdur Rakib.Abdur@uwe.ac.uk
Senior Lecturer in Mobile Security



Contributors

Elias Pimenidis
Editor

Plamen Angelov
Editor

Chrisina Jayne
Editor

Antonios Papaleonidas
Editor

Mehmet Aydin
Editor

Abstract

Exploring search spaces is one of the most unpredictable challenges that has attracted the interest of researchers for decades. One way to handle unpredictability is to characterise the search spaces and take actions accordingly. A well-characterised search space can assist in mapping the problem states to a set of operators for generating new problem states. In this paper, a landscape analysis-based set of features has been analysed using the most renown machine learning approaches to determine the optimal feature set. However, in order to deal with problem complexity and induce commonality for transferring experience across domains, the selection of the most representative features remains crucial. The proposed approach analyses the predictivity of a set of features in order to determine the best categorization.

Citation

Durgut, R., Aydin, M. E., Ihshaish, H., & Rakib, A. (2022). Analysing the predictivity of features to characterise the search space. In E. Pimenidis, P. Angelov, C. Jayne, A. Papaleonidas, & M. Aydin (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2022 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV (1-13). https://doi.org/10.1007/978-3-031-15937-4_1

Conference Name Artificial Neural Networks and Machine Learning – ICANN 2022 31st International Conference on Artificial Neural Networks, Bristol, UK
Start Date Sep 6, 2022
End Date Sep 9, 2022
Acceptance Date Jul 2, 2022
Online Publication Date Sep 7, 2022
Publication Date 2022
Deposit Date Sep 6, 2022
Publicly Available Date Sep 8, 2024
Publisher Springer Verlag
Volume 13532 LNCS
Pages 1-13
Series Title Lecture Notes in Computer Science (LNCS, volume 13532)
Series Number 13532
Edition Proceedings; Part IV
Book Title Artificial Neural Networks and Machine Learning – ICANN 2022 31st International Conference on Artificial Neural Networks, Bristol, UK, September 6–9, 2022, Proceedings; Part IV
Chapter Number 1
ISBN 9783031159367
DOI https://doi.org/10.1007/978-3-031-15937-4_1
Keywords Feature analysis, Search space characterisation, Supervised machine learning
Public URL https://uwe-repository.worktribe.com/output/9952793
Publisher URL https://link.springer.com/book/10.1007/978-3-031-15937-4
Related Public URLs https://link.springer.com/conference/icann
Additional Information First Online: 7 September 2022; Conference Acronym: ICANN; Conference Name: International Conference on Artificial Neural Networks; Conference City: Bristol; Conference Country: United Kingdom; Conference Year: 2022; Conference Start Date: 6 September 2022; Conference End Date: 9 September 2022; Conference Number: 31; Conference ID: icann2022; Conference URL: https://e-nns.org/icann2022/; Type: Single-blind; Conference Management System: EasyChair; Number of Submissions Sent for Review: 561; Number of Full Papers Accepted: 255; Number of Short Papers Accepted: 4; Acceptance Rate of Full Papers: 45% - The value is computed by the equation "Number of Full Papers Accepted / Number of Submissions Sent for Review * 100" and then rounded to a whole number.; Average Number of Reviews per Paper: 3; External Reviewers Involved: No