Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Transforming classifier scores into accurate multiclass probability estimates
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Training structural SVMs when exact inference is intractable
Proceedings of the 25th international conference on Machine learning
MULAN: A Java Library for Multi-Label Learning
The Journal of Machine Learning Research
Classifier chains for multi-label classification
Machine Learning
Bayesian chain classifiers for multidimensional classification
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Beam search algorithms for multilabel learning
Machine Learning
Dependent binary relevance models for multi-label classification
Pattern Recognition
| Hi-index | 0.00 |
Multilabel learning is an extension of binary classification that is both challenging and practically important. Recently, a method for multilabel learning called probabilistic classifier chains (PCCs) was proposed with numerous appealing properties, such as conceptual simplicity, flexibility, and theoretical justification. However, PCCs suffer from the computational issue of having inference that is exponential in the number of tags, and the practical issue of being sensitive to the suitable ordering of the tags while training. In this paper, we show how the classical technique of beam search may be used to solve both these problems. Specifically, we show how to use beam search to perform tractable test time inference, and how to integrate beam search with training to determine a suitable tag ordering. Experimental results on a range of multilabel datasets show that these proposed changes dramatically extend the practical viability of PCCs.