Abstract
This paper aims at developing new models to combine the best of two paradigms: the performance of ensembles and the transparency of choice models. Specifically, the work explores several blind methods to build ensembles of single choice-based models. They were fit using a dataset that includes rational, emotional and attentional variables, all of them gathered from humans during a choice task. Two main strategies, 1-Learner and N-Learners type ensembles, were analyzed in terms of their accuracy as well as frequency of best-model metrics. The results point out the superior performance of N-Learners ensembles and show the potential of personalized arrangements.
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We want to acknowledge the collaboration of Movistar and Neurologyca on building the dataset used in this paper.
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Almomani, A., Sánchez, E. Blind methods to build choice-based ensembles. Nat Comput 21, 589–601 (2022). https://doi.org/10.1007/s11047-021-09852-4
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DOI: https://doi.org/10.1007/s11047-021-09852-4