Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
What size net gives valid generalization?
Neural Computation
Original Contribution: Stacked generalization
Neural Networks
Toward Efficient Agnostic Learning
Machine Learning - Special issue on computational learning theory, COLT'92
The nature of statistical learning theory
The nature of statistical learning theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Collective multi-label classification
Proceedings of the 14th ACM international conference on Information and knowledge management
The VC dimension of k-fold union
Information Processing Letters
Feature set decomposition for decision trees
Intelligent Data Analysis
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Genetic algorithm-based feature set partitioning for classification problems
Pattern Recognition
Random k-Labelsets: An Ensemble Method for Multilabel Classification
ECML '07 Proceedings of the 18th European conference on Machine Learning
Multi-label Classification Using Ensembles of Pruned Sets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Feature selection for multi-label naive Bayes classification
Information Sciences: an International Journal
Classifier Chains for Multi-label Classification
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
k-Fold unions of low-dimensional concept classes
Information Processing Letters
Multi-label learning by exploiting label dependency
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Classification Using Ensemble Methods
Pattern Classification Using Ensemble Methods
Constructing a Fast Algorithm for Multi-label Classification with Support Vector Data Description
GRC '10 Proceedings of the 2010 IEEE International Conference on Granular Computing
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Multi-label classification exhibits several challenges not present in the binary case. The labels may be interdependent, so that the presence of a certain label affects the probability of other labels' presence. Thus, exploiting dependencies among the labels could be beneficial for the classifier's predictive performance. Surprisingly, only a few of the existing algorithms address this issue directly by identifying dependent labels explicitly from the dataset. In this paper we propose new approaches for identifying and modeling existing dependencies between labels. One principal contribution of this work is a theoretical confirmation of the reduction in sample complexity that is gained from unconditional dependence. Additionally, we develop methods for identifying conditionally and unconditionally dependent label pairs; clustering them into several mutually exclusive subsets; and finally, performing multi-label classification incorporating the discovered dependencies. We compare these two notions of label dependence (conditional and unconditional) and evaluate their performance on various benchmark and artificial datasets. We also compare and analyze labels identified as dependent by each of the methods. Moreover, we define an ensemble framework for the new methods and compare it to existing ensemble methods. An empirical comparison of the new approaches to existing base-line and state-of-the-art methods on 12 various benchmark datasets demonstrates that in many cases the proposed single-classifier and ensemble methods outperform many multi-label classification algorithms. Perhaps surprisingly, we discover that the weaker notion of unconditional dependence plays the decisive role.