Decision trees and multi-valued attributes
Machine intelligence 11
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
A Monotonic Measure for Optimal Feature Selection
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Empirical Evaluation of Feature Subset Selection Based on a Real-World Data Set
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Evaluating Feature Selection Methods for Learning in Data Mining Applications
HICSS '98 Proceedings of the Thirty-First Annual Hawaii International Conference on System Sciences-Volume 5 - Volume 5
Feature Selection Algorithms: A Survey and Experimental Evaluation
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
An introduction to variable and feature selection
The Journal of Machine Learning Research
Fast Branch & Bound Algorithms for Optimal Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Testing the significance of attribute interactions
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
A distance-based branch and bound feature selection algorithm
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
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With the increasing size of databases, feature selection has become a relevant and challenging problem for the area of knowledge discovery in databases. An effective feature selection strategy can significantly reduce the data mining processing time, improve the predicted accuracy, and help to understand the induced models, as they tend to be smaller and make more sense to the user. Many feature selection algorithms assumed that the attributes are independent between each other given the class, which can produce models with redundant attributes and/or exclude sets of attributes that are relevant when considered together. In this paper, an effective best first search algorithm, called buBF, for feature selection is described. buBF uses a novel heuristic function based on n-way entropy to capture inter-dependencies among variables. It is shown that buBF produces more accurate models than other state-of-the-art feature selection algorithms when compared on several real and synthetic datasets. Specifically we apply buBF to a Mexican Electric Billing database and obtain satisfactory results.