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Supervised Dimensionality Reduction for the Algorithm Selection Problem. /
Notice, Danielle; Pavlidis, Nicos; Kheiri, Ahmed.
Advances in Computational Intelligence Systems: Contributions Presented at the 23rd UK Workshop on Computational Intelligence (UKCI 2024), September 2-4, 2024, Ulster University, Belfast, UK. ed. / Huiru Zheng; David Glass; Maurice Mulvenna; Jun Liu; Hui Wang. Cham: Springer, 2025. p. 85-97 (Advances in Intelligent Systems and Computing ; Vol. 1462).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Notice, D, Pavlidis, N & Kheiri, A 2025,
Supervised Dimensionality Reduction for the Algorithm Selection Problem. in H Zheng, D Glass, M Mulvenna, J Liu & H Wang (eds),
Advances in Computational Intelligence Systems: Contributions Presented at the 23rd UK Workshop on Computational Intelligence (UKCI 2024), September 2-4, 2024, Ulster University, Belfast, UK. Advances in Intelligent Systems and Computing , vol. 1462, Springer, Cham, pp. 85-97.
https://doi.org/10.1007/978-3-031-78857-4_7
APA
Notice, D., Pavlidis, N., & Kheiri, A. (2025).
Supervised Dimensionality Reduction for the Algorithm Selection Problem. In H. Zheng, D. Glass, M. Mulvenna, J. Liu, & H. Wang (Eds.),
Advances in Computational Intelligence Systems: Contributions Presented at the 23rd UK Workshop on Computational Intelligence (UKCI 2024), September 2-4, 2024, Ulster University, Belfast, UK (pp. 85-97). (Advances in Intelligent Systems and Computing ; Vol. 1462). Springer.
https://doi.org/10.1007/978-3-031-78857-4_7
Vancouver
Notice D, Pavlidis N, Kheiri A.
Supervised Dimensionality Reduction for the Algorithm Selection Problem. In Zheng H, Glass D, Mulvenna M, Liu J, Wang H, editors, Advances in Computational Intelligence Systems: Contributions Presented at the 23rd UK Workshop on Computational Intelligence (UKCI 2024), September 2-4, 2024, Ulster University, Belfast, UK. Cham: Springer. 2025. p. 85-97. (Advances in Intelligent Systems and Computing ). doi: 10.1007/978-3-031-78857-4_7
Author
Bibtex
@inproceedings{1531023d2c7b4a29806b774a4cb36146,
title = "Supervised Dimensionality Reduction for the Algorithm Selection Problem",
abstract = "Instance space analysis extends the algorithm selection framework by enabling the visualisation of problem instances via dimensionality reduction (DR). The lower dimensional projection can also be used as input to predict algorithm performance, or to perform algorithm selection. In this paper we consider two supervised DR methods - partial least squares (PLS) and linear discriminant analysis (LDA) - both as visualisation tools and for the purpose of constructing classification models for algorithm selection. Multinomial logistic regression models are used for the classification problem. We compare PLS and LDA to DR methods previously used in this context on three combinatorial optimisation problems, and show that these methods are as competitive.",
author = "Danielle Notice and Nicos Pavlidis and Ahmed Kheiri",
year = "2025",
month = jan,
day = "8",
doi = "10.1007/978-3-031-78857-4_7",
language = "English",
isbn = "9783031788567",
series = "Advances in Intelligent Systems and Computing ",
publisher = "Springer",
pages = "85--97",
editor = "Huiru Zheng and David Glass and Maurice Mulvenna and Jun Liu and Hui Wang",
booktitle = "Advances in Computational Intelligence Systems",
}
RIS
TY - GEN
T1 - Supervised Dimensionality Reduction for the Algorithm Selection Problem
AU - Notice, Danielle
AU - Pavlidis, Nicos
AU - Kheiri, Ahmed
PY - 2025/1/8
Y1 - 2025/1/8
N2 - Instance space analysis extends the algorithm selection framework by enabling the visualisation of problem instances via dimensionality reduction (DR). The lower dimensional projection can also be used as input to predict algorithm performance, or to perform algorithm selection. In this paper we consider two supervised DR methods - partial least squares (PLS) and linear discriminant analysis (LDA) - both as visualisation tools and for the purpose of constructing classification models for algorithm selection. Multinomial logistic regression models are used for the classification problem. We compare PLS and LDA to DR methods previously used in this context on three combinatorial optimisation problems, and show that these methods are as competitive.
AB - Instance space analysis extends the algorithm selection framework by enabling the visualisation of problem instances via dimensionality reduction (DR). The lower dimensional projection can also be used as input to predict algorithm performance, or to perform algorithm selection. In this paper we consider two supervised DR methods - partial least squares (PLS) and linear discriminant analysis (LDA) - both as visualisation tools and for the purpose of constructing classification models for algorithm selection. Multinomial logistic regression models are used for the classification problem. We compare PLS and LDA to DR methods previously used in this context on three combinatorial optimisation problems, and show that these methods are as competitive.
U2 - 10.1007/978-3-031-78857-4_7
DO - 10.1007/978-3-031-78857-4_7
M3 - Conference contribution/Paper
SN - 9783031788567
T3 - Advances in Intelligent Systems and Computing
SP - 85
EP - 97
BT - Advances in Computational Intelligence Systems
A2 - Zheng, Huiru
A2 - Glass, David
A2 - Mulvenna, Maurice
A2 - Liu, Jun
A2 - Wang, Hui
PB - Springer
CY - Cham
ER -
