Communications of the ACM
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
The cascade-correlation learning architecture
Advances in neural information processing systems 2
Advances in neural information processing systems 2
C4.5: programs for machine learning
C4.5: programs for machine learning
A “thermal” perceptron learning rule
Neural Computation
An introduction to genetic algorithms
An introduction to genetic algorithms
Wrappers for performance enhancement and oblivious decision graphs
Wrappers for performance enhancement and oblivious decision graphs
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Control-Sensitive Feature Selection for Lazy Learners
Artificial Intelligence Review - Special issue on lazy learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature subset selection by Bayesian network-based optimization
Artificial Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Feature Selection via Discretization
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Neural Computation
Discovering Patterns in EEG-Signals: Comparative Study of a Few Methods
ECML '93 Proceedings of the European Conference on Machine Learning
Second Order Derivatives for Network Pruning: Optimal Brain Surgeon
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
Genetic Algorithms as a Tool for Restructuring Feature Space Representations
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Process-Specific Information for Learning Electronic Negotiation Outcomes
Fundamenta Informaticae
Diversity-Based Feature Selection from Neural Network with Low Computational Cost
Neural Information Processing
A Statistical Approach to Incremental Induction of First-Order Hierarchical Knowledge Bases
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Exhaustive and heuristic search approaches for learning a software defect prediction model
Engineering Applications of Artificial Intelligence
Relevance metrics to reduce input dimensions in artificial neural networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
A new hybrid ant colony optimization algorithm for feature selection
Expert Systems with Applications: An International Journal
Bernoulli trials based feature selection for crater detection
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Process-Specific Information for Learning Electronic Negotiation Outcomes
Fundamenta Informaticae
A maximum-margin genetic algorithm for misclassification cost minimizing feature selection problem
Expert Systems with Applications: An International Journal
A survey on feature selection methods
Computers and Electrical Engineering
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Variable selection, the process of identifying input variables that are relevant to a particular learning problem, has received much attention in the learning community. Methods that employ a learning algorithm as a part of the selection process (wrappers) have been shown to outperform methods that select variables independently from the learning algorithm (filters), but only at great computational expense. We present a randomized wrapper algorithm whose computational requirements are within a constant factor of simply learning in the presence of all input variables, provided that the number of relevant variables is small and known in advance. We then show how to remove the latter assumption, and demonstrate performance on several problems.