In one embodiment, a set of training data consisting of inliers may be obtained. A supervised classification model may be trained using the set of training data to identify outliers. The supervised classification model may be applied to generate an anomaly score for a data point. It may be determined whether the data point is an outlier based, at least in part, upon the anomaly score.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: obtaining a set of training data consisting of inliers; training a supervised classification model using the set of training data to identify outliers; applying the supervised classification model to generate an anomaly score for a data point; and determining whether the data point is an outlier based, at least in part, upon the anomaly score. 2. The method as recited in claim 1, wherein the supervised classification model comprises a supervised two-class classification model that estimates a relative importance measure, the relative importance measure being a ratio of training and test data densities. 3. The method as recited in claim 1, wherein the supervised classification model comprises a gradient boosted decision tree (GBDT) algorithm. 4. The method as recited in claim 1, wherein the supervised classification model performs feature selection to select one or more features upon which to generate anomaly scores for data points. 5. The method as recited in claim 1, wherein the set of training data comprises email account data corresponding to non-spammers, and wherein determining whether the data point is an outlier comprises determining whether the data point is a compromised email account. 6. The method as recited in claim 1, wherein the set of training data comprises images of semiconductors, and wherein determining whether the data point is an outlier comprises determining whether the data point corresponds to a faulty semiconductor. 7. The method as recited in claim 1, wherein the set of training data comprises speaker data. 8. An apparatus, comprising: a processor; and a memory storing thereon computer-readable instructions, the computer-readable instructions being configured to: obtain a set of training data consisting of inliers; train a supervised classification model using the set of training data to identify outliers; apply the supervised classification model to generate an anomaly score for a data point; and determine whether the data point is an outlier based, at least in part, upon the anomaly score. 9. The apparatus as recited in claim 8, wherein the supervised classification model comprises a supervised two-class classification model that estimates a relative importance measure, the relative importance measure being a ratio of training and test data densities. 10. The apparatus as recited in claim 8, wherein the supervised classification model comprises a gradient boosted decision tree (GBDT) algorithm. 11. The apparatus as recited in claim 8, wherein the supervised classification model performs feature selection to select one or more features upon which to generate anomaly scores for data points. 12. The apparatus as recited in claim 8, wherein the set of training data comprises email account data corresponding to non-spammers, and wherein determining whether the data point is an outlier comprises determining whether the data point is a compromised email account. 13. The apparatus as recited in claim 8, wherein the set of training data comprises images of semiconductors, and wherein determining whether the data point is an outlier comprises determining whether the data point corresponds to a faulty semiconductor. 14. The apparatus as recited in claim 8, wherein the set of training data comprises speaker data. 15. A non-transitory computer-readable storage medium, comprising: instructions for obtaining a set of training data consisting of inliers; instructions for training a supervised classification model using the set of training data to identify outliers; instructions for applying the supervised classification model to generate an anomaly score for a data point; and instructions for determining whether the data point is an outlier based, at least in part, upon the anomaly score. 16. The non-transitory computer-readable storage medium as recited in claim 15, wherein the supervised classification model comprises a supervised two-class classification model that estimates a relative importance measure, the relative importance measure being a ratio of training and test data densities. 17. The non-transitory computer-readable storage medium as recited in claim 15, wherein the supervised classification model comprises a gradient boosted decision tree (GBDT) algorithm. 18. The non-transitory computer-readable storage medium as recited in claim 15, wherein the supervised classification model performs feature selection to select one or more features upon which to generate anomaly scores for data points. 19. The non-transitory computer-readable storage medium as recited in claim 15, wherein the set of training data comprises email account data corresponding to non-spammers, and wherein determining whether the data point is an outlier comprises determining whether the data point is a compromised email account. 20. The non-transitory computer-readable storage medium as recited in claim 15, wherein the set of training data comprises image data or speaker data.