  Feature selection is beneficial for improving the performance of general
machine learning tasks by extracting an informative subset from the
high-dimensional features. Conventional feature selection methods usually
ignore the class imbalance problem, thus the selected features will be biased
towards the majority class. Considering that F-measure is a more reasonable
performance measure than accuracy for imbalanced data, this paper presents an
effective feature selection algorithm that explores the class imbalance issue
by optimizing F-measures. Since F-measure optimization can be decomposed into a
series of cost-sensitive classification problems, we investigate the
cost-sensitive feature selection by generating and assigning different costs to
each class with rigorous theory guidance. After solving a series of
cost-sensitive feature selection problems, features corresponding to the best
F-measure will be selected. In this way, the selected features will fully
represent the properties of all classes. Experimental results on popular
benchmarks and challenging real-world data sets demonstrate the significance of
cost-sensitive feature selection for the imbalanced data setting and validate
the effectiveness of the proposed method.
