A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers

In order to analyze the differences between the models using the original and the transformed targets, we can take a look at the aggregated (absolute) feature effect, averaged over

Because we want to illustrate that our global approach is able to solve difficult numerical problems with high accuracy, we choose to present the results obtained with a fine mesh (n

We present a general definition of the margin and propose a C-bound designed for majority vote-based ensemble methods when we want to combine complex output classifiers.. We

To compare the two methods relying on a two-steps ma- chine learning approach with our convolutional neural network and our Siamese network, we used the Base B1 with k-folds

For example, combination of unsupervised learning (self-organizing map) and reinforcement learn- ing can be seen in several works such as [8] where the input signals of the

Therefore, as multivariate contains more information, it might be reasonable to take advantage of ensemble learning approach and apply it to the best fit models to improve

Second resubmission step: “PELICAN: deeP architecturE for the LIght Curve ANalysis” Johanna Pasquet, J´ erˆ ome Pasquet, Marc Chaumont and Dominique Fouchez. Astronomy

De la même manière qu’un comportement donné peut être envisagé sous l’angle exclusif du rapport à soi ou également comme ayant un impact sur autrui, un