An adaptive symbolic discretization scheme for the classification of temporal datasets using NSGA-II

Márquez-Grajales, Aldo; Acosta-Mesa, Héctor-Gabriel; Mezura-Montes, Efrén

doi:10.1109/ropec.2017.8261674

Cited by 6 publications

(9 citation statements)

References 14 publications

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“…Therefore, the experiment's goal in this document is to reduce such computational costs by using surrogate models without losing the classification power. Moreover, each experiment was designed to prove if our proposal improves the prediction power (model fidelity) regarding the proposal described in [33].…”

Section: Experimental Designmentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

“…• Can sMODiTS increase the model fidelity regarding [33]? This question arises in analyzing the prediction power of sMODiTS and the proposal introduced in [33] compared to eMODiTS (original model). The results will seek to achieve the first research objective and are presented in Section 3.2.…”

Section: Experimental Designmentioning

confidence: 99%

“…Nevertheless, as far as state-of-the-art has been reviewed, surrogate models have been scarcely implemented in time series discretization. Márquez et al [33] is one of the few works in which surrogate models were implemented to minimize the computational cost by identifying an appropriate discretization scheme for temporal data. The researchers applied surrogate models to the enhanced multi-objective symbolic discretization for time series (eMODiTS) [20].…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, our primary motivation for this research is to extend the findings of [33], modifying the model update process. The proposed methodology evaluates the Pareto front using the original objective functions at each generation (individual-based strategy) and the current population at regular intervals (generation-based strategy).…”

Section: Introductionmentioning

confidence: 99%

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A Surrogate-Assisted Symbolic Time-Series Discretization Using Multi-breakpoints and a Multi-objective Evolutionary Algorithm

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Acosta-Mesa

et al. 2024

Preprint

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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 requires a computational cost for evaluating each objective function. Therefore, surrogate models were implemented to minimize this disadvantage. Nevertheless, each solution found by eMODiTS is a different-sized vector, so the surrogate model must be able to handle data sets under this characteristic. Consequently, this work's contribution lies in analyzing the surrogate models' implementation on the time series discretization, where each potential scheme is a real-number different-sized vector. For this reason, the surrogate model proposed was k-nearest Neighbors for regression with Dynamic Time Warping as a distance measure. Results suggest our proposal finds a suitable approximation to the final eMODiTS solutions with a functions evaluation reduction rate between 15% and 95%. However, according to Pareto front performance measures, the proposal Pareto front is competitive compared to the eMODiTS Pareto front, reaching an average Generational Distance (GD) between 0.0447 and 0.0536. Moreover, the average Hypervolume Ratio (HVR) ranges between 0.334 and 0.3891. Finally, our proposal compared against SAX-based methods presents a similar behavior regarding classification tasks and statistical tests.

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Section: Experimental Designmentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%