Analysing the Predictivity of Features to Characterise the Search Space

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

doi:10.1007/978-3-031-15937-4_1

Analysing the Predictivity of Features to Characterise the Search Space

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

Centre for Future Transport and Cities

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Artificial Neural Networks and Machine Learning - ICANN 2022
Editors	Elias Pimenidis, Mehmet Aydin, Plamen Angelov, Chrisina Jayne, Antonios Papaleonidas
Publisher	Springer
Pages	1-13
Number of pages	13
ISBN (Electronic)	978-3-031-15937-4
ISBN (Print)	978-3-031-15936-7
DOIs	https://doi.org/10.1007/978-3-031-15937-4_1
Publication status	E-pub ahead of print - 7 Sept 2022
Event	31st International Conference on Artificial Neural Networks - The University of the West of England (UWE Bristol) , Bristol, United Kingdom Duration: 6 Sept 2022 → 9 Sept 2022 https://e-nns.org/icann2022/conference-programme/

Publication series

Name	Lecture Notes in Computer Sciene
Publisher	Springer
Volume	13532
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	31st International Conference on Artificial Neural Networks
Abbreviated title	ICANN22
Country/Territory	United Kingdom
City	Bristol
Period	6/09/22 → 9/09/22
Internet address	https://e-nns.org/icann2022/conference-programme/

Keywords

feature analysis
search space characterisation
supervised machine learning

Access to Document

10.1007/978-3-031-15937-4_1

Cite this

Durgut, R., Aydin, M. E., Ihshaish, H., & Abdur, R. (2022). Analysing the Predictivity of Features to Characterise the Search Space. In E. Pimenidis, M. Aydin, P. Angelov, C. Jayne, & A. Papaleonidas (Eds.), Artificial Neural Networks and Machine Learning - ICANN 2022 (pp. 1-13). (Lecture Notes in Computer Sciene; Vol. 13532). Springer . Advance online publication. https://doi.org/10.1007/978-3-031-15937-4_1

Analysing the Predictivity of Features to Characterise the Search Space. / Durgut, Rafet; Aydin, Mehmet Emin; Ihshaish, Hisham et al.
Artificial Neural Networks and Machine Learning - ICANN 2022. ed. / Elias Pimenidis; Mehmet Aydin; Plamen Angelov; Chrisina Jayne; Antonios Papaleonidas. Springer , 2022. p. 1-13 (Lecture Notes in Computer Sciene; Vol. 13532).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

Durgut, R, Aydin, ME, Ihshaish, H & Abdur, R 2022, Analysing the Predictivity of Features to Characterise the Search Space. in E Pimenidis, M Aydin, P Angelov, C Jayne & A Papaleonidas (eds), Artificial Neural Networks and Machine Learning - ICANN 2022. Lecture Notes in Computer Sciene, vol. 13532, Springer , pp. 1-13, 31st International Conference on Artificial Neural Networks, Bristol, United Kingdom, 6/09/22. https://doi.org/10.1007/978-3-031-15937-4_1

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Analysing the Predictivity of Features to Characterise the Search Space

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Keywords

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