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Anomaly is a deviation from the normal behavior of the system and anomaly detection techniques try to identify unusual instances based on deviation from the normal data. In this work, I propose a machine-learning algorithm, referred to as Artificial Contrasts,

Anomaly is a deviation from the normal behavior of the system and anomaly detection techniques try to identify unusual instances based on deviation from the normal data. In this work, I propose a machine-learning algorithm, referred to as Artificial Contrasts, for anomaly detection in categorical data in which neither the dimension, the specific attributes involved, nor the form of the pattern is known a priori. I use RandomForest (RF) technique as an effective learner for artificial contrast. RF is a powerful algorithm that can handle relations of attributes in high dimensional data and detect anomalies while providing probability estimates for risk decisions.

I apply the model to two simulated data sets and one real data set. The model was able to detect anomalies with a very high accuracy. Finally, by comparing the proposed model with other models in the literature, I demonstrate superior performance of the proposed model.
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    Title
    • Anomaly detection in categorical datasets with artificial contrasts
    Contributors
    Date Created
    2016
    Resource Type
  • Text
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    Note
    • thesis
      Partial requirement for: M.S., Arizona State University, 2016
    • bibliography
      Includes bibliographical references (pages 51-53)
    • Field of study: Industrial engineering

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    by Seyyedehnasim Mousavi

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