Abstract
Ordinal classifier cascades (OCCs) are basic machine learning tools in the field of ordinal classification (OC) that consist of a sequence of classification models (CMs). Each of the CMs is trained in combination with a specific subtask of the initial OC task. OCC architectures make use of a data set’s ordinal class structure by simply arranging the CMs with respect to the corresponding class order (e.g., small - medium - large). Recently, we proposed bidirectional OCC (bOCC) architectures that combine two basic one-directional OCCs, based on a person-independent pain intensity recognition scenario, in combination with support vector machines. In the current study, we further analyse the effectiveness of bOCC architectures. To this end, we evaluate our proposed approach based on different OC benchmark data sets. Additionally, we analyse the proposed bOCCs in combination with two different classification models. Our outcomes indicate that it seems to be beneficial to replace basic pairwise one-directional OCCs by the pairwise bOCC architecture, in general.
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Acknowledgments
The work of Peter Bellmann and Friedhelm Schwenker is supported by the project Multimodal recognition of affect over the course of a tutorial learning experiment (SCHW623/7-1) funded by the German Research Foundation (DFG). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research. Hans A. Kestler acknowledges funding from the German Science Foundation (DFG, 217328187 (SFB 1074) and 288342734 (GRK HEIST)). Hans A. Kestler also acknowledges funding from the German Federal Ministry of Education and Research (BMBF) e:MED confirm (id 01ZX1708C) and TRAN-SCAN VI - PMTR-pNET (id 01KT1901B).
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Bellmann, P., Lausser, L., Kestler, H.A., Schwenker, F. (2021). Experimental Analysis of Bidirectional Pairwise Ordinal Classifier Cascades. In: Torsello, A., Rossi, L., Pelillo, M., Biggio, B., Robles-Kelly, A. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2021. Lecture Notes in Computer Science(), vol 12644. Springer, Cham. https://doi.org/10.1007/978-3-030-73973-7_6
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