Proceedings of the Eighth International Conference on Probabilistic Graphical Models, PMLR 52:13-24, 2016.
Abstract
Multidimensional classification has become one of the most relevant topics in view of the many domains that require a vector of class values to be assigned to a vector of given features. The popularity of multidimensional Bayesian network classifiers has increased in the last few years due to their expressive power and the existence of methods for learning different families of these models. The problem with this approach is that the computational cost of using the learned models is usually high, especially if there are a lot of class variables. Class-bridge decomposability means that the multidimensional classification problem can be divided into multiple subproblems for these models. In this paper, we prove that class-bridge decomposability can also be used to guarantee the tractability of the models. We also propose a strategy for efficiently bounding their inference complexity, providing a simple learning method with an order-based search that obtains tractable multidimensional Bayesian network classifiers. Experimental results show that our approach is competitive with other methods in the state of the art and ensures the tractability of the learned models.
@InProceedings{pmlr-v52-benjumeda16,
title = {Learning Tractable Multidimensional {B}ayesian Network Classifiers},
author = {Marco Benjumeda and Concha Bielza and Pedro Larrañaga},
booktitle = {Proceedings of the Eighth International Conference on Probabilistic Graphical Models},
pages = {13--24},
year = {2016},
editor = {Alessandro Antonucci and Giorgio Corani and Cassio Polpo Campos}},
volume = {52},
series = {Proceedings of Machine Learning Research},
address = {Lugano, Switzerland},
month = {06--09 Sep},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v52/benjumeda16.pdf},
url = {http://proceedings.mlr.press/v52/benjumeda16.html},
abstract = {Multidimensional classification has become one of the most relevant topics in view of the many domains that require a vector of class values to be assigned to a vector of given features. The popularity of multidimensional Bayesian network classifiers has increased in the last few years due to their expressive power and the existence of methods for learning different families of these models. The problem with this approach is that the computational cost of using the learned models is usually high, especially if there are a lot of class variables. Class-bridge decomposability means that the multidimensional classification problem can be divided into multiple subproblems for these models. In this paper, we prove that class-bridge decomposability can also be used to guarantee the tractability of the models. We also propose a strategy for efficiently bounding their inference complexity, providing a simple learning method with an order-based search that obtains tractable multidimensional Bayesian network classifiers. Experimental results show that our approach is competitive with other methods in the state of the art and ensures the tractability of the learned models.}
}
%0 Conference Paper
%T Learning Tractable Multidimensional Bayesian Network Classifiers
%A Marco Benjumeda
%A Concha Bielza
%A Pedro Larrañaga
%B Proceedings of the Eighth International Conference on Probabilistic Graphical Models
%C Proceedings of Machine Learning Research
%D 2016
%E Alessandro Antonucci
%E Giorgio Corani
%E Cassio Polpo Campos}
%F pmlr-v52-benjumeda16
%I PMLR
%J Proceedings of Machine Learning Research
%P 13--24
%U http://proceedings.mlr.press
%V 52
%W PMLR
%X Multidimensional classification has become one of the most relevant topics in view of the many domains that require a vector of class values to be assigned to a vector of given features. The popularity of multidimensional Bayesian network classifiers has increased in the last few years due to their expressive power and the existence of methods for learning different families of these models. The problem with this approach is that the computational cost of using the learned models is usually high, especially if there are a lot of class variables. Class-bridge decomposability means that the multidimensional classification problem can be divided into multiple subproblems for these models. In this paper, we prove that class-bridge decomposability can also be used to guarantee the tractability of the models. We also propose a strategy for efficiently bounding their inference complexity, providing a simple learning method with an order-based search that obtains tractable multidimensional Bayesian network classifiers. Experimental results show that our approach is competitive with other methods in the state of the art and ensures the tractability of the learned models.
TY - CPAPER
TI - Learning Tractable Multidimensional Bayesian Network Classifiers
AU - Marco Benjumeda
AU - Concha Bielza
AU - Pedro Larrañaga
BT - Proceedings of the Eighth International Conference on Probabilistic Graphical Models
PY - 2016/08/15
DA - 2016/08/15
ED - Alessandro Antonucci
ED - Giorgio Corani
ED - Cassio Polpo Campos}
ID - pmlr-v52-benjumeda16
PB - PMLR
SP - 13
DP - PMLR
EP - 24
L1 - http://proceedings.mlr.press/v52/benjumeda16.pdf
UR - http://proceedings.mlr.press/v52/benjumeda16.html
AB - Multidimensional classification has become one of the most relevant topics in view of the many domains that require a vector of class values to be assigned to a vector of given features. The popularity of multidimensional Bayesian network classifiers has increased in the last few years due to their expressive power and the existence of methods for learning different families of these models. The problem with this approach is that the computational cost of using the learned models is usually high, especially if there are a lot of class variables. Class-bridge decomposability means that the multidimensional classification problem can be divided into multiple subproblems for these models. In this paper, we prove that class-bridge decomposability can also be used to guarantee the tractability of the models. We also propose a strategy for efficiently bounding their inference complexity, providing a simple learning method with an order-based search that obtains tractable multidimensional Bayesian network classifiers. Experimental results show that our approach is competitive with other methods in the state of the art and ensures the tractability of the learned models.
ER -
Benjumeda, M., Bielza, C. & Larrañaga, P.. (2016). Learning Tractable Multidimensional Bayesian Network Classifiers. Proceedings of the Eighth International Conference on Probabilistic Graphical Models, in PMLR 52:13-24
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