Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Knowledge Discovery in Multi-label Phenotype Data
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
On Model Selection Consistency of Lasso
The Journal of Machine Learning Research
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Feature selection for multi-label naive Bayes classification
Information Sciences: an International Journal
Joint covariate selection and joint subspace selection for multiple classification problems
Statistics and Computing
Speeding Up Greedy Forward Selection for Regularized Least-Squares
ICMLA '10 Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications
Random k-Labelsets for Multilabel Classification
IEEE Transactions on Knowledge and Data Engineering
MULAN: A Java Library for Multi-Label Learning
The Journal of Machine Learning Research
Feature selection for multi-label classification problems
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
Efficient Learning with Partially Observed Attributes
The Journal of Machine Learning Research
Greedy Regularized Least-Squares for Multi-task Learning
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
An extensive experimental comparison of methods for multi-label learning
Pattern Recognition
Adaptive Forward-Backward Greedy Algorithm for Learning Sparse Representations
IEEE Transactions on Information Theory
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We consider the problem of learning sparse linear models for multi-label prediction tasks under a hard constraint on the number of features. Such budget constraints are important in domains where the acquisition of the feature values is costly. We propose a greedy multi-label regularized least-squares algorithm that solves this problem by combining greedy forward selection search with a cross-validation based selection criterion in order to choose, which features to include in the model. We present a highly efficient algorithm for implementing this procedure with linear time and space complexities. This is achieved through the use of matrix update formulas for speeding up feature addition and cross-validation computations. Experimentally, we demonstrate that the approach allows finding sparse accurate predictors on a wide range of benchmark problems, typically outperforming the multi-task lasso baseline method when the budget is small.