Learning Boolean concepts in the presence of many irrelevant features
Artificial Intelligence
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Projection-based measure for efficient feature selection
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - IBERAMIA '02
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Massive data sets have become common in many applications making the task of finding an optimum subset of attributes extremely difficult. Traditional feature selection techniques can be very inefficient in high dimensional data, especially when the subset evaluation is obtained through a learning algorithm. We describe a method based on the statistical significance of adding a feature from a ranked-list to the final subset. To measure individual feature, we propose a new simple and fast criterion based on the projections of data set elements onto each attribute.