Filter Feature Selection for One-Class Classification

In one-class classification problems all training examples belong to a single class. The absence of counter-examples represents a challenge to traditional Machine Learning and pre-processing techniques. This is the case of various feature selection techniques for labeled data. The selection of the most relevant features from a dataset usually benefits the performance obtained by classification algorithms. Despite the relevance of this issue, few techniques have been proposed for feature selection in one-class classification problems. Moreover, most of the existent techniques are wrapper approaches, which have to rely on a specific classification algorithm for feature selection, or aggregation techniques. This paper proposes a new filter feature selection approach for one-class classification. First, five feature selection measures from different paradigms are here employed or adapted to the one-class scenario. Next, the feature rankings produced by these measures are combined using different aggregation strategies. The proposed approach was able to reduce the size of the feature sets while maintaining or even improving the predictive performance obtained by the one-class classifier.