C4.5: programs for machine learning
C4.5: programs for machine learning
Redundancy based feature selection for microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Feature selection with conditional mutual information maximin in text categorization
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
Analysis of breast feeding data using data mining methods
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
Discovering prediction model for environmental distribution maps
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
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For classification of health data, we propose in this paper a fast and accurate feature selection method, FIEBIT (Feature Inclusion and Exclusion Based on Information Theory). FIEBIT selects the most relevant and non-redundant features using Conditional Mutual Information (CMU) while excluding irrelevant and redundant features according to the comparison among Individual Symmetrical Uncertainty (ISU) and Combined Symmetrical Uncertainty (CSU). Small feature subsets are selected before classification without compromising the classification accuracy. In addition, the size of the feature subset is determined automatically. Our preliminary empirical results on health data with hundreds of features suggest FIEBIT is efficient and effective in comparison with representative feature selection methods.