IEEE Transactions on Pattern Analysis and Machine Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
The handbook of brain theory and neural networks
Combining predictors: comparison of five meta machine learning methods
Information Sciences: an International Journal
Information Retrieval
Convergence of a block coordinate descent method for nondifferentiable minimization
Journal of Optimization Theory and Applications
Machine Learning
Mixture of experts classification using a hierarchical mixture model
Neural Computation
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Chi2: Feature Selection and Discretization of Numeric Attributes
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Convex Optimization
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Information Sciences: an International Journal
Penalized Model-Based Clustering with Application to Variable Selection
The Journal of Machine Learning Research
Adaptive mixtures of local experts
Neural Computation
EfficientL1regularized logistic regression
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
Unified video annotation via multigraph learning
IEEE Transactions on Circuits and Systems for Video Technology
A dynamic classifier ensemble selection approach for noise data
Information Sciences: an International Journal
Simultaneous feature selection and classification using kernel-penalized support vector machines
Information Sciences: an International Journal
Heterogeneous feature selection by group lasso with logistic regression
Proceedings of the international conference on Multimedia
Bayesian hierarchical mixtures of experts
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Eigenclassifiers for combining correlated classifiers
Information Sciences: an International Journal
Training regression ensembles by sequential target correction and resampling
Information Sciences: an International Journal
Ensemble Manifold Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
Assemble New Object Detector With Few Examples
IEEE Transactions on Image Processing
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A useful strategy to deal with complex classification scenarios is the ''divide and conquer'' approach. The mixture of experts (MoE) technique makes use of this strategy by jointly training a set of classifiers, or experts, that are specialized in different regions of the input space. A global model, or gate function, complements the experts by learning a function that weighs their relevance in different parts of the input space. Local feature selection appears as an attractive alternative to improve the specialization of experts and gate function, particularly, in the case of high dimensional data. In general, subsets of dimensions, or subspaces, are usually more appropriate to classify instances located in different regions of the input space. Accordingly, this work contributes with a regularized variant of MoE that incorporates an embedded process for local feature selection using L"1 regularization. Experiments using artificial and real-world datasets provide evidence that the proposed method improves the classical MoE technique, in terms of accuracy and sparseness of the solution. Furthermore, our results indicate that the advantages of the proposed technique increase with the dimensionality of the data.