TY - JOUR
T1 - A scalable pairwise class interaction framework for multidimensional classification
AU - Arias, Jacinto
AU - Gámez, Jose A.
AU - Nielsen, Thomas Dyhre
AU - Puerta, José M.
PY - 2016
Y1 - 2016
N2 - We present a general framework for multidimensional classification that cap- tures the pairwise interactions between class variables. The pairwise class inter- actions are encoded using a collection of base classifiers (Phase 1), for which the class predictions are combined in a Markov random field that is subsequently used for multidimensional inference (Phase 2); thus, the framework can be positioned between multilabel Bayesian classifiers and label transformation-based approaches. Our proposal leads to a general framework supporting a wide range of base classifiers in the first phase as well as different inference methods in the second phase. We describe the basic framework and its main properties, as well as strategies for ensuring the scalability of the framework. We include a detailed experimental evaluation based on a range of publicly available databases. Here we analyze the overall performance of the framework and we test the behavior of the different scalability strategies proposed. A comparison with other state-of-the-art multidimensional classifiers show that the proposed framework either outperforms or is competitive with the tested straw-men methods.
AB - We present a general framework for multidimensional classification that cap- tures the pairwise interactions between class variables. The pairwise class inter- actions are encoded using a collection of base classifiers (Phase 1), for which the class predictions are combined in a Markov random field that is subsequently used for multidimensional inference (Phase 2); thus, the framework can be positioned between multilabel Bayesian classifiers and label transformation-based approaches. Our proposal leads to a general framework supporting a wide range of base classifiers in the first phase as well as different inference methods in the second phase. We describe the basic framework and its main properties, as well as strategies for ensuring the scalability of the framework. We include a detailed experimental evaluation based on a range of publicly available databases. Here we analyze the overall performance of the framework and we test the behavior of the different scalability strategies proposed. A comparison with other state-of-the-art multidimensional classifiers show that the proposed framework either outperforms or is competitive with the tested straw-men methods.
U2 - 10.1016/j.ijar.2015.07.007
DO - 10.1016/j.ijar.2015.07.007
M3 - Journal article
SN - 0888-613X
VL - 68
SP - 194
EP - 210
JO - International Journal of Approximate Reasoning
JF - International Journal of Approximate Reasoning
ER -