Anomaly Detection Using an Ensemble of Feature Models

  title={Anomaly Detection Using an Ensemble of Feature Models},
  author={Keith Noto and Carla E. Brodley and Donna K. Slonim},
  journal={2010 IEEE International Conference on Data Mining},
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… CONTINUE READING
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