Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Large Margin Classification Using the Perceptron Algorithm
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
A new approximate maximal margin classification algorithm
The Journal of Machine Learning Research
An introduction to variable and feature selection
The Journal of Machine Learning Research
Distributional word clusters vs. words for text categorization
The Journal of Machine Learning Research
Dimensionality reduction via sparse support vector machines
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online feature selection for pixel classification
ICML '05 Proceedings of the 22nd international conference on Machine learning
Prediction, Learning, and Games
Prediction, Learning, and Games
Online Passive-Aggressive Algorithms
The Journal of Machine Learning Research
Direct convex relaxations of sparse SVM
Proceedings of the 24th international conference on Machine learning
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
Tracking the best hyperplane with a simple budget Perceptron
Machine Learning
A review of feature selection techniques in bioinformatics
Bioinformatics
The Forgetron: A Kernel-Based Perceptron on a Budget
SIAM Journal on Computing
Confidence-weighted linear classification
Proceedings of the 25th international conference on Machine learning
The projectron: a bounded kernel-based Perceptron
Proceedings of the 25th international conference on Machine learning
Non-monotonic feature selection
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Sparse Online Learning via Truncated Gradient
The Journal of Machine Learning Research
Efficient Online and Batch Learning Using Forward Backward Splitting
The Journal of Machine Learning Research
Distance based feature selection for clustering microarray data
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
Online multiple kernel learning: algorithms and mistake bounds
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
Double Updating Online Learning
The Journal of Machine Learning Research
Collaborative online learning of user generated content
Proceedings of the 20th ACM international conference on Information and knowledge management
IEEE Transactions on Information Theory
Online group feature selection
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Most studies of online learning require accessing all the attributes/features of training instances. Such a classical setting is not always appropriate for real-world applications when data instances are of high dimensionality or the access to it is expensive to acquire the full set of attributes/features. To address this limitation, we investigate the problem of Online Feature Selection (OFS) in which the online learner is only allowed to maintain a classifier involved a small and fixed number of features. The key challenge of Online Feature Selection is how to make accurate prediction using a small and fixed number of active features. This is in contrast to the classical setup of online learning where all the features are active and can be used for prediction. We address this challenge by studying sparsity regularization and truncation techniques. Specifically, we present an effective algorithm to solve the problem, give the theoretical analysis, and evaluate the empirical performance of the proposed algorithms for online feature selection on several public datasets. We also demonstrate the application of our online feature selection technique to tackle real-world problems of big data mining, which is significantly more scalable than some well-known batch feature selection algorithms. The encouraging results of our experiments validate the efficacy and efficiency of the proposed techniques for large-scale applications.