arXiv:2608. 12057v1 Announce Type: new Abstract: Feature selection is one of the most important and fundamental tasks in data mining, tackled by a family of methods with an established set of evaluation techniques to measure the quality of a specific method.
By Hafiz Saud Arshad, Muhammad Rajabinasab, Arthur Zimek
arXiv:2608.12057v4 Announce Type: replace
Abstract: Feature selection is one of the most important and fundamental tasks in data mining, tackled by a family of methods with an established set of eval...
By Hafiz Saud Arshad, Muhammad Rajabinasab, Arthur Zimek
arXiv:2605. 04954v2 Announce Type: replace-cross Abstract: Per-instance algorithm selection (PIAS) takes advantage of complementarity between a set of algorithms by deciding which algorithm to run on a given instance.
By Koen van der Blom, Diederick Vermetten
arXiv:2606. 15115v1 Announce Type: new Abstract: Multi-objective optimization (MOO) has emerged as a powerful approach to solving complex optimization problems involving multiple objectives.
By Yiyi Zhu, Yaolin Wen, Xiang Xia, Xin An, Hanyi Si, Xiang Shu, Yangde Fu, Liang Dou, Hong Qian
arXiv:2607. 19031v1 Announce Type: new Abstract: Automated algorithm selection in black-box optimization typically relies on supervised models that map landscape features to algorithm performance labels.
By Yihang Lu, Tome Eftimov, Carola Doerr
arXiv:1907.06994v2 Announce Type: replace-cross
Abstract: Mixtures of experts (MoE) are conditional mixture models in which both the mixing proportions and the component densities depend on the predi...
By Thin Nguyen-Van, Faicel Chamroukhi, Ha Hoang Van, Bao Tuyen Huynh