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[75200] Artykuł: ECG time series classification via genetic-fuzzy approach based on accuracy-interpretability trade-off optimizationCzasopismo: IEEE International Conference on Fuzzy Systems Strony: 1761-1768ISSN: 1098-7584 ISBN: 978-1-5090-6020-7 Wydawca: IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA Opublikowano: 2018 Seria wydawnicza: IEEE International Conference on Fuzzy Systems Autorzy / Redaktorzy / Twórcy Grupa MNiSW: Konferencja Informatyczna Punkty MNiSW: 140 Klasyfikacja Web of Science: Proceedings Paper DOI Web of Science |
This paper presents the application of our multiobjective-evolutionary-optimization-based (MOEOA-based) design technique of fuzzy rule-based classifiers with genetically optimized accuracy-interpretability trade-off to the problems of ECG time series data classification. First, the ECG200 time series data set coming from the UCR Time Series Classification Archive and used in our experiments is briefly characterized. Then, main components of our approach are outlined. For the purpose of comparison, two MOEOAs are employed in our experiments, i.e., the well-known Strength Pareto Evolutionary Algorithm 2 (SPEA2) and our SPEA2’s generalization (referred to as SPEA3) characterized by better performance indices. Our results for the considered ECG time series data are compared with the results of 16 alternative methods, in order to present the advantages (in terms of the optimization of the classifiers’ accuracy-interpretability trade-off) of our approach.