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.
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- Partial requirement for: M.S., Arizona State University, 2016Note typethesis
- Includes bibliographical references (pages 51-53)Note typebibliography
- Field of study: Industrial engineering