Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
A study of thresholding strategies for text categorization
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Ensembling neural networks: many could be better than all
Artificial Intelligence
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Classifier Chains for Multi-label Classification
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Improving multilabel classification performance by using ensemble of multi-label classifiers
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
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While it is known that multiple classifier systems can be effective also in multi-label problems, only the classifier fusion approach has been considered so far. In this paper we focus on the classifier selection approach instead. We propose an implementation of this approach specific to multi-label classifiers, based on selecting the outputs of a possibly different subset of multi-label classifiers for each class. We then derive static selection criteria for the macro- and micro-averaged F measure, which is widely used in multi-label problems. Preliminary experimental results show that the considered selection strategy can exploit the complementarity of an ensemble of multi-label classifiers more effectively than selection approaches analogous to the ones used in single-label problems, which select the outputs of the same classifier subset for all classes. Our results also show that the derived selection criteria can provide a better trade-off between the macro- and micro-averaged F measure, despite it is known that an increase in either of them is usually attained at the expense of the other one.