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Theoretical Foundations of Forward Feature Selection Methods based on Mutual Information
Feature selection problems arise in a variety of applications, such as microarray analysis, clinical prediction, text categorization, image classification and face recognition, multi-label learning, and classification of internet traffic.
Theoretical Evaluation of Feature Selection Methods based on Mutual Information
Feature selection methods are usually evaluated by wrapping specific classifiers and datasets in the evaluation process, resulting very often in unfair comparisons between methods.
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