Constructing Training Sets for Outlier Detection

Abstract

Outlier detection often works in an unsupervised manner due to the difficulty of obtaining enough training data. Since outliers are rare, one has to label a very large dataset to include enough outliers in the training set, with which classifiers could sufficiently learn the concept of outliers. Labeling a large training set is costly for most applications. However, we could just label suspected instances identified by unsupervised methods. In this way, the number of instances to be labeled could be greatly reduced. Based on this idea, we propose CISO, an algorithm Constructing training set by Identifying Suspected Outliers. In this algorithm, instances in a pool are first ranked by an unsupervised outlier detection algorithm. Then, suspected instances are selected and hand-labeled, and all remaining instances receive label of in-lier. As such, all instances in the pool are labeled and used in the training set. We also propose Budgeted CISO (BCISO), with which user could set a fixed budget for labeling. Experiments show that both algorithms achieve good performance compared to other methods when the same amount of labeling effort are used.