Description
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.
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.
Details
Title
- Anomaly detection in categorical datasets with artificial contrasts
Contributors
- Mousavi, Seyyedehnasim (Author)
- Runger, George C. (Thesis advisor)
- Wu, Teresa (Committee member)
- Kim, Sunghoon (Committee member)
- Arizona State University (Publisher)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2016
Resource Type
Collections this item is in
Note
- thesisPartial requirement for: M.S., Arizona State University, 2016
- bibliographyIncludes bibliographical references (pages 51-53)
- Field of study: Industrial engineering
Citation and reuse
Statement of Responsibility
by Seyyedehnasim Mousavi