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Surrogate-Assisted Symbolic Time-Series Discretization Using Multi-Breakpoints and a Multi-Objective Evolutionary Algorithm

Authors :
Aldo Márquez-Grajales
Efrén Mezura-Montes
Héctor-Gabriel Acosta-Mesa
Fernando Salas-Martínez
Source :
Mathematical and Computational Applications, Vol 29, Iss 5, p 78 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

The enhanced multi-objective symbolic discretization for time series (eMODiTS) method employs a flexible discretization scheme using different value cuts for each non-equal time interval, which incurs a high computational cost for evaluating each objective function. It is essential to mention that each solution found by eMODiTS is a different-sized vector. Previous work was performed where surrogate models were implemented to reduce the computational cost to solve this problem. However, low-fidelity approximations were obtained concerning the original model. Consequently, our main objective is to propose an improvement to this work, modifying the updating process of the surrogate models to minimize their disadvantages. This improvement was evaluated based on classification, predictive power, and computational cost, comparing it against the original model and ten discretization methods reported in the literature. The results suggest that the proposal achieves a higher fidelity to the original model than previous work. It also achieved a computational cost reduction rate between 15% and 80% concerning the original model. Finally, the classification error of our proposal is similar to eMODiTS and maintains its behavior compared to the other discretization methods.

Details

Language :
English
ISSN :
22978747 and 1300686X
Volume :
29
Issue :
5
Database :
Directory of Open Access Journals
Journal :
Mathematical and Computational Applications
Publication Type :
Academic Journal
Accession number :
edsdoj.2e6f00fb6d5040a0a641e99c11863d12
Document Type :
article
Full Text :
https://doi.org/10.3390/mca29050078