Abstract
The paper explores uninformed methods to build ensembles using aggregations of single choice models. The research aims at developing new models to combine the performance of ensembles with the transparency of choice models. The dataset used to fit the models included rational, emotional and attentional features that were used as explanatory variables of user’s choice. The results point out the superior performance of bagging methods to build optimal choice-based ensembles.
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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. (2019). Uninformed Methods to Build Optimal Choice-Based Ensembles. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science(), vol 11487. Springer, Cham. https://doi.org/10.1007/978-3-030-19651-6_6
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DOI: https://doi.org/10.1007/978-3-030-19651-6_6
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