Early classification of time series: Cost-based multiclass algorithms
Published in IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2021
Recommended citation: Zafar, P. E., Achenchabe, Y., Bondu, A., Cornuéjols, A., & Lemaire, V. (2021, October). Early classification of time series: Cost-based multiclass algorithms. In 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 1-10). IEEE. https://ieeexplore.ieee.org/abstract/document/9564134
Early classification of time series assigns each time series to one of a set of pre-defined classes using as few measurements as possible while preserving a high accuracy. This implies solving online the trade-off between the earliness and the prediction accuracy. This has been formalized in previous work where a cost-based framework taking into account both the cost of misclassification and the cost of delaying the decision has been proposed. The best resulting method, called Economy- γ , is unfortunately so far limited to binary classification problems. This paper presents a set of six new methods that extend the Economy- γ method in order to solve multiclass classification problems. Extensive experiments on 33 datasets allowed us to compare the performance of the six proposed approaches to the state-of-the-art one. The results show that: (i) all proposed methods perform significantly better than the state of the art one; (ii) the best way to extend Economy- γ to multiclass problems is to use a confidence score, either the Gini index or the maximum probability.