Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Consistency-based search in feature selection
Artificial Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
The class imbalance problem: A systematic study
Intelligent Data Analysis
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
The ANNIGMA-wrapper approach to fast feature selection for neuralnets
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In many classification problems, and in particular in medical domains, it is common to have an unbalanced class distribution. This pose problems to classifiers as they tend to perform poorly in the minority class which is often the class of interest. One commonly used strategy that to improve the classification performance is to select a subset of relevant features. Feature selection algorithms, however, have not been designed to favour the classification performance of the minority class. In this paper, we present a novel filter feature selection algorithm, called FSMC, for unbalanced data sets. FSMC selects attributes that have minority class distributions significantly different from the majority class distributions. FSMC is fast, simple, selects a small number of features and outperforms in most cases other feature selection algorithms in terms of global accuracy and in terms of performance measures for the minority class such as precision, recall, F-measure and ROC values.