In this article we propose a conceptual framework to study ensembles of conformal predictors (CP), that we call Ensemble Predictors (EP). Our approach is inspired by the application of imprecise probabilities in information fusion. Based on the proposed framework, we study, for the first time in the literature, the theoretical properties of CP ensembles in a general setting, by focusing on simple and commonly used possibilistic combination rules. We also illustrate the applicability of the proposed methods in the setting of multivariate time-series classification, showing that these methods provide better performance (in terms of both robustness, conservativeness, accuracy and running time) than both standard classification algorithms and other combination rules proposed in the literature, on a large set of benchmarks from the UCR time series archive.

Campagner, A., Barandas, M., Folgado, D., Gamboa, H., Cabitza, F. (2024). Ensemble Predictors: Possibilistic Combination of Conformal Predictors for Multivariate Time Series Classification. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1-12 [10.1109/tpami.2024.3388097].

Ensemble Predictors: Possibilistic Combination of Conformal Predictors for Multivariate Time Series Classification

Campagner, Andrea
Primo
;
Cabitza, Federico
2024

Abstract

In this article we propose a conceptual framework to study ensembles of conformal predictors (CP), that we call Ensemble Predictors (EP). Our approach is inspired by the application of imprecise probabilities in information fusion. Based on the proposed framework, we study, for the first time in the literature, the theoretical properties of CP ensembles in a general setting, by focusing on simple and commonly used possibilistic combination rules. We also illustrate the applicability of the proposed methods in the setting of multivariate time-series classification, showing that these methods provide better performance (in terms of both robustness, conservativeness, accuracy and running time) than both standard classification algorithms and other combination rules proposed in the literature, on a large set of benchmarks from the UCR time series archive.
Articolo in rivista - Articolo scientifico
Benchmark testing; Computational modeling; Conformal Prediction; Ensemble Learning; Ensemble learning; Focusing; Machine Learning; Multivariate Time Series; Possibility theory; Robustness; Task analysis; Time series analysis;
English
12-apr-2024
2024
1
12
reserved
Campagner, A., Barandas, M., Folgado, D., Gamboa, H., Cabitza, F. (2024). Ensemble Predictors: Possibilistic Combination of Conformal Predictors for Multivariate Time Series Classification. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1-12 [10.1109/tpami.2024.3388097].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/473202
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