Gokhan Gulgezen

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In addition to accuracy, stability is also a measure of success for a feature selection algorithm. Stability could especially be a concern when the number of samples in a data set is small and the dimensionality is high. In this study, we introduce a stability measure, and perform both accuracy and stability measurements of MRMR (Minimum Redundancy Maximum(More)
In this paper we describe an extension of the information theoretical FCBF (Fast Correlation Based Feature Selection) algorithm. The extension, called FCBF#, enables FCBF to select any given size of feature subset and it selects features in a different order than the FCBF. We find out that the extended FCBF algorithm results in more accurate classifiers.
Feature selection methods help machine learning algorithms produce faster and more accurate solutions, because they reduce the input dimensionality and they can eliminate irrelevant or redundant features. Entropy based feature selection algorithms, such as MRMR (Minimum Redundancy Maximum Relevance, [1]) and FCBF (Fast Correlation-Based Filter, [2]) are(More)
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