Accelerated pattern search with variable solution size for simultaneous instance selection and generation

Le, Hoang Lam; Neri, Ferrante; Landa-Silva, Dario; Triguero, Isaac

Abstract

The search for the optimum in a mixed continuous-combinatorial space is a challenging task since it requires operators that handle both natures of the search domain. Instance reduction (IR), an important pre-processing technique in data science, is often performed in separated stages, combining instance selection (IS) first, and sub-sequently instance generation (IG). This paper investigates a fast optimisation approach for IR considering the two stages at once. This approach, namely Accelerated Pattern Search with Variable Solution Size (APS-VSS), is characterised by a variable solution size, an accelerated objective function computation, and a single-point memetic structure designed for IG. APS-VSS is composed of a global search crossover and three local searches (LS). The global operator prevents premature convergence to local optima, whilst the three LS algorithms optimise the reduced set (RS). Furthermore, by using the k-nearest neighbours algorithm as a base classifier, APS-VSS exploits the search logic of the LS to accelerate, by orders of magnitude, objective function computation. The experiments show that APS-VSS outperforms existing algorithms using the single-point structure, and is statistically as competitive as state-of-the-art IR techniques regarding accuracy and reduction rates, while reducing significantly the runtime.

Citation

Le, H. L., Neri, F., Landa-Silva, D., & Triguero, I. (2022, July). Accelerated pattern search with variable solution size for simultaneous instance selection and generation. Poster presented at Genetic and Evolutionary Computation Conference Companion (GECCO 2022), Boston, USA and online

• Presentation Conference Type: Poster
• Conference Name: Genetic and Evolutionary Computation Conference Companion (GECCO 2022)
• Start Date: Jul 9, 2022
• End Date: Jul 13, 2022
• Acceptance Date: May 19, 2022
• Online Publication Date: Jul 19, 2022
• Publication Date: Jul 19, 2022
• Deposit Date: Oct 22, 2024
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Book Title: GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion
• ISBN: 978-1-4503-9268-6
• DOI: https://doi.org/10.1145/3520304.3529020
• Public URL: https://nottingham-repository.worktribe.com/output/10357974
• Publisher URL: https://dl.acm.org/doi/10.1145/3520304.3529020