Machine Learning
Training algorithms for linear text classifiers
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
High-performing feature selection for text classification
Proceedings of the eleventh international conference on Information and knowledge management
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Feature selection using linear classifier weights: interaction with classification models
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A theoretical characterization of linear SVM-based feature selection
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Analysis of recursive feature elimination methods
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Stability of Feature Selection Algorithms
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Stability of Feature Selection Algorithms
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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Searching the feature space for a subset yielding optimum performance tends to be expensive, especially in applications where the cardinality of the feature space is high (e.g., text categorization). This is particularly true for massive datasets and learning algorithms with worse than linear scaling factors. Linear Support Vector Machines (SVMs) are among the top performers in the text classification domain and often work best with very rich feature representations. Even they however benefit from reducing the number of features, sometimes to a large extent. In this work we propose alternatives to exact re-induction of SVM models during the search for the optimum feature subset. The approximations offer substantial benefits in terms of computational efficiency. We are able to demonstrate that no significant compromises in terms of model quality are made and, moreover, in some cases gains in accuracy can be achieved.