We propose to tackle the cost-sensitive learning problem, where each feature is associated to a particular acquisition cost. We propose a new model with the following key properties: (i) it acquires features in an adaptive way, (ii) features can be acquired per block (several at a time) so that this model can deal with high dimensional data, and (iii) it relies on representation-learning ideas. The effectiveness of this approach is demonstrated on several experiments considering a variety of datasets and with different cost settings.
CITATION STYLE
Contardo, G., Denoyer, L., & Artières, T. (2016). Recurrent neural networks for adaptive feature acquisition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9949 LNCS, pp. 591–599). Springer Verlag. https://doi.org/10.1007/978-3-319-46675-0_65
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