Abstract
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.
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Acknowledgment
This work has been partially supported by the French National Agency (ANR) in the frame of its FRQC program (TEEC, project number ANR-16-FRQC-0009-03).
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Sazadaly, M., Pinchon, P., Fagot, A., Prevost, L., Bertrand, M.M. (2018). Fast and Accurate Affect Prediction Using a Hierarchy of Random Forests. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_75
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