Anomaly Detection Using an Ensemble of Feature Models

We present a new approach to semi-supervised anomaly detection. Given a set of training examples believed to come from the same distribution or class, the task is to learn a model that will be able to distinguish examples in the future that do not belong to the same class. Traditional approaches typically compare the position of a new data point to the set of "normal" training data points in a chosen representation of the feature space. For some data sets, the normal data may not have discernible positions in feature space, but do have consistent relationships among some features that fail to appear in the anomalous examples. Our approach learns to predict the values of training set features from the values of other features. After we have formed an ensemble of predictors, we apply this ensemble to new data points. To combine the contribution of each predictor in our ensemble, we have developed a novel, information-theoretic anomaly measure that our experimental results show ...

• Type: Journal
• Year: 2010
• Where: ICDM
• Authors: Keith Noto, Carla E. Brodley, Donna K. Slonim
• Keywords: Data Mining, Data Points, Data Sets, Feature Space, ICDM 2010