Finding MAPs for belief networks is NP-hard
Artificial Intelligence
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
IEEE Transactions on Knowledge and Data Engineering
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
A discretization algorithm based on Class-Attribute Contingency Coefficient
Information Sciences: an International Journal
Decision trees for hierarchical multi-label classification
Machine Learning
Multi-Objective Learning of Multi-Dimensional Bayesian Classifiers
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Inference and Learning in Multi-dimensional Bayesian Network Classifiers
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Classifier Chains for Multi-label Classification
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Automated heart wall motion abnormality detection from ultrasound images using Bayesian networks
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multi-dimensional classification with Bayesian networks
International Journal of Approximate Reasoning
Learning and inference in probabilistic classifier chains with beam search
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Artificial Intelligence in Medicine
An efficient probabilistic framework for multi-dimensional classification
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Beam search algorithms for multilabel learning
Machine Learning
Probabilistic multi-label classification with sparse feature learning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Information Sciences: an International Journal
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In multidimensional classification the goal is to assign an instance to a set of different classes. This task is normally addressed either by defining a compound class variable with all the possible combinations of classes (label power-set methods, LPMs) or by building independent classifiers for each class (binary-relevance methods, BRMs). However, LPMs do not scale well and BRMs ignore the dependency relations between classes. We introduce a method for chaining binary Bayesian classifiers that combines the strengths of classifier chains and Bayesian networks for multidimensional classification. The method consists of two phases. In the first phase, a Bayesian network (BN) that represents the dependency relations between the class variables is learned from data. In the second phase, several chain classifiers are built, such that the order of the class variables in the chain is consistent with the class BN. At the end we combine the results of the different generated orders. Our method considers the dependencies between class variables and takes advantage of the conditional independence relations to build simplified models. We perform experiments with a chain of naïve Bayes classifiers on different benchmark multidimensional datasets and show that our approach outperforms other state-of-the-art methods.