A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Constructive induction using a non-greedy strategy for feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Learning Boolean concepts in the presence of many irrelevant features
Artificial Intelligence
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence Review - Special issue on lazy learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Using Rough Sets with Heuristics for Feature Selection
Journal of Intelligent Information Systems
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Feature Selection via Discretization
IEEE Transactions on Knowledge and Data Engineering
An Implementation of Logical Analysis of Data
IEEE Transactions on Knowledge and Data Engineering
A Modified Chi2 Algorithm for Discretization
IEEE Transactions on Knowledge and Data Engineering
Feature Selection Using Rough Sets Theory
ECML '93 Proceedings of the European Conference on Machine Learning
A Monotonic Measure for Optimal Feature Selection
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Feature Selection via Set Cover
KDEX '97 Proceedings of the 1997 IEEE Knowledge and Data Engineering Exchange Workshop
Consistency-based search in feature selection
Artificial Intelligence
Fast Branch & Bound Algorithms for Optimal Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expert Systems with Applications: An International Journal
Multi-objective genetic algorithm evaluation in feature selection
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
An effective feature selection method using dynamic information criterion
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Feature selection using hierarchical feature clustering
Proceedings of the 20th ACM international conference on Information and knowledge management
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Engineering Applications of Artificial Intelligence
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The use of feature selection can improve accuracy, efficiency, applicability and understandability of a learning process. For this reason, many methods of automatic feature selection have been developed. Some of these methods are based on the search of the features that allows the data set to be considered consistent. In a search problem we usually evaluate the search states, in the case of feature selection we measure the possible feature sets. This paper reviews the state of the art of consistency based feature selection methods, identifying the measures used for feature sets. An in-deep study of these measures is conducted, including the definition of a new measure necessary for completeness. After that, we perform an empirical evaluation of the measures comparing them with the highly reputed wrapper approach. Consistency measures achieve similar results to those of the wrapper approach with much better efficiency.