Hierarchical systems are powerful tools to deal with non-linear data with a high variability. We show in this paper that regressing a bounded variable on such data is a challenging task. As an alternate, we propose here a two-step process. First, an ensemble of ordinal classifiers affect the observation to a given range of the variable to predict and a discrete estimate of the variable. Then, a regressor is trained locally on this range and its neighbors and provides a finer continuous estimate. Experiments on affect audio data from the AVEC’2014 and AV+EC’2015 challenges show that this cascading process can be compared favorably to the state of the art and challengers results.
CITATION STYLE
Sazadaly, M., Pinchon, P., Fagot, A., Prevost, L., & Bertrand, M. M. (2018). Fast and accurate affect prediction using a hierarchy of random forests. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11139 LNCS, pp. 771–781). Springer Verlag. https://doi.org/10.1007/978-3-030-01418-6_75
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