Time series classifier recommendation by a meta-learning approach

This work addresses time series classifier recommendation for the first time in the literature by considering several recommendation forms or meta-targets: classifier accuracies, complete ranking, top-M ranking, best set and best classifier. For this, an ad-hoc set of quick estimators of the accuracies of the candidate classifiers (landmarkers) are designed, which are used as predictors for the recommendation system. The performance of our recommender is compared with the performance of a standard method for non-sequential data and a set of baseline methods, which our method outperforms in 7 of the 9 considered scenarios. Since some meta-targets can be inferred from the predictions of other more finegrained meta-targets, the last part of the work addresses the hierarchical inference of meta-targets. The experimentation suggests that, in many cases, a single model is sufficient to output many types of meta targets with competitive results.(c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This study investigates time series classifier recommendation for the first time, considering various recommendation forms or meta-targets. The researchers design a set of quick estimators as predictors for the recommendation system. Experimental results show that the proposed method outperforms other methods in most scenarios, and a hierarchical inference method for meta-targets is also proposed.