Matching Items (1)
Filtering by
- All Subjects: Statistics
![155102-Thumbnail Image.png](https://d1rbsgppyrdqq4.cloudfront.net/s3fs-public/styles/width_400/public/2021-09/155102-Thumbnail%20Image.png?versionId=AXipbOJrWJWAbk5tZGLm4WPF5KuVHXGC&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIASBVQ3ZQ42ZLA5CUJ/20240618/us-west-2/s3/aws4_request&X-Amz-Date=20240618T132405Z&X-Amz-SignedHeaders=host&X-Amz-Expires=120&X-Amz-Signature=8d9a1b55f4747ff5bcca6742f1a0374c4dd26c781eadb963775ed64d8290514f&itok=OKwO7IYB)
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.
ContributorsMousavi, Seyyedehnasim (Author) / Runger, George C. (Thesis advisor) / Wu, Teresa (Committee member) / Kim, Sunghoon (Committee member) / Arizona State University (Publisher)
Created2016