TY - GEN
T1 - A computationally efficient approach for solving RBSC-based formulation of the subset selection problem
AU - Furuya, Kohei
AU - Yucel, Zeynep
AU - Supitayakul, Parisa
AU - Monden, Akito
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This study focuses on a specific type of subset selection problem, which is constrained in terms of the rank bi-serial correlation (RBSC) coefficient of the outputs. For solving such problems, we propose an approach with several advantages such as (i) providing a clear insight into the feasibility of the problem with respect to the hyper-parameters, (ii) being non-iterative, (iii) having a foreseeable running time, and (iv) with the potential to yield non-deterministic (diverse) outputs. In particular, the proposed approach is based on starting from a composition of subsets with an extreme value of the RBSC coefficient (e.g. ρ=1) and swapping certain elements of the subsets in order to adjust ρ into the desired range. The proposed method is superior to the previously proposed RBSC-SubGen, which attempts to solve the problem before confirming its feasibility, taking random steps, and has unforeseeable running times and saturation issues.
AB - This study focuses on a specific type of subset selection problem, which is constrained in terms of the rank bi-serial correlation (RBSC) coefficient of the outputs. For solving such problems, we propose an approach with several advantages such as (i) providing a clear insight into the feasibility of the problem with respect to the hyper-parameters, (ii) being non-iterative, (iii) having a foreseeable running time, and (iv) with the potential to yield non-deterministic (diverse) outputs. In particular, the proposed approach is based on starting from a composition of subsets with an extreme value of the RBSC coefficient (e.g. ρ=1) and swapping certain elements of the subsets in order to adjust ρ into the desired range. The proposed method is superior to the previously proposed RBSC-SubGen, which attempts to solve the problem before confirming its feasibility, taking random steps, and has unforeseeable running times and saturation issues.
KW - rank bi-serial correlation
KW - ranking-and-selection problem
KW - subset selection
UR - http://www.scopus.com/inward/record.url?scp=85139567977&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85139567977&partnerID=8YFLogxK
U2 - 10.1109/IIAIAAI55812.2022.00076
DO - 10.1109/IIAIAAI55812.2022.00076
M3 - Conference contribution
AN - SCOPUS:85139567977
T3 - Proceedings - 2022 12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022
SP - 341
EP - 347
BT - Proceedings - 2022 12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022
A2 - Matsuo, Tokuro
A2 - Takamatsu, Kunihiko
A2 - Ono, Yuichi
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022
Y2 - 2 July 2022 through 7 July 2022
ER -