A Local Search with a Surrogate Assisted Option for Instance Reduction

Neri, Ferrante; Triguero, Isaac

Abstract

© 2020, Springer Nature Switzerland AG. In data mining, instance reduction is a key data pre-processing step that simplifies and cleans raw data, by either selecting or creating new samples, before applying a learning algorithm. This usually yields to a complex large scale and computationally expensive optimisation problem which has been typically tackled by sophisticated population-based metaheuristics. Unlike the recent literature, in order to accomplish this target, this article proposes the use of a simple local search algorithm and its integration with an optional surrogate assisted model. This local search, in accordance with variable decomposition techniques for large scale problems, perturbs an n-dimensional vector along the directions identified by its design variables one by one. Empirical results in 40 small data sets show that, despite its simplicity, the proposed baseline local search on its own is competitive with more complex algorithms representing the state-of-the-art for instance reduction in classification problems. The use of the proposed local surrogate model enables a reduction of the computationally expensive objective function calls with accuracy test results overall comparable with respect to its baseline counterpart.

Citation

Neri, F., & Triguero, I. (2020). A Local Search with a Surrogate Assisted Option for Instance Reduction. https://doi.org/10.1007/978-3-030-43722-0_37

• Conference Name: International Conference on the Applications of Evolutionary Computation (Part of EvoStar)
• Start Date: Apr 15, 2020
• End Date: Apr 17, 2020
• Acceptance Date: Jan 9, 2020
• Online Publication Date: Apr 9, 2020
• Publication Date: 2020
• Deposit Date: Jan 10, 2020
• Publicly Available Date: Jan 14, 2020
• Publisher: Springer Verlag
• Volume: 12104 LNCS
• Pages: 578-594
• Series Title: Lecture Notes in Computer Science
• ISBN: 9783030437213
• DOI: https://doi.org/10.1007/978-3-030-43722-0_37
• Keywords: Instance reduction; Instance generation; Computationally expensive problems; Surrogate assisted algorithms; Local search; Pattern Search
• Public URL: https://nottingham-repository.worktribe.com/output/3702667
• Publisher URL: https://link.springer.com/chapter/10.1007%2F978-3-030-43722-0_37
• Additional Information: First Online: 9 April 2020; Conference Acronym: EvoApplications; Conference Name: International Conference on the Applications of Evolutionary Computation (Part of EvoStar); Conference City: Seville; Conference Country: Spain; Conference Year: 2020; Conference Start Date: 15 April 2020; Conference End Date: 17 April 2020; Conference Number: 23; Conference ID: evoapplications2020; Conference URL: http://www.evostar.org/2020/evoapps/; Type: Double-blind; Conference Management System: easychair; Number of Submissions Sent for Review: 62; Number of Full Papers Accepted: 44; Number of Short Papers Accepted: 0; Acceptance Rate of Full Papers: 71% - The value is computed by the equation "Number of Full Papers Accepted / Number of Submissions Sent for Review * 100" and then rounded to a whole number.; Average Number of Reviews per Paper: 2.68; Average Number of Papers per Reviewer: 1.25; External Reviewers Involved: No