FAST: A ROC-based Feature Selection Metric for Small Samples and Imbalanced Data Classification Problems

摘要

The class imbalance problem is encountered in a large number of practical applications of machine learning and data mining, for example, information retrieval and filtering, and the detection of credit card fraud. It has been widely realized that this imbalance raises issues that are either nonexistent or less severe compared to balanced class cases and often results in a classifier's suboptimal performance. This is even more true when the imbalanced data are also high dimensional. In such cases, feature selection methods are critical to achieve optimal performance. In this paper, we propose a new feature selection method, Feature Assessment by Sliding Thresholds (FAST), which is based on the area under a ROC curve generated by moving the decision boundary of a single feature classifier with thresholds placed using an even-bin distribution. FAST is compared to two commonly-used feature selection methods, correlation coefficient and RELevance In Estimating Features (RELIEF), for imbalanced data classification. The experimental results obtained on text mining, mass spectrometry, and microarray data sets showed that the proposed method outperformed both RELIEF and correlation methods on skewed data sets and was comparable on balanced data sets; when small number of features is preferred, the classification performance of the proposed method was significantly improved compared to correlation and RELIEF-based methods.