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Anomaly Detection in Categorical Datasets with Artificial Contrasts


Abstract 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 ... (more)
Created Date 2016
Contributor Mousavi, Seyyedehnasim (Author) / Runger, George (Advisor) / Wu, Teresa (Committee member) / Kim, Sunghoon (Committee member) / Arizona State University (Publisher)
Subject Statistics
Type Masters Thesis
Extent 63 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Masters Thesis Industrial Engineering 2016
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis