Leveraging One-Class SVM and Semantic Analysis to Detect Anomalous Content

Author(s)/Creator(s)

Ozgur Yilmazel, Syracuse UniversityFollow
Svetlana Symonenko, Syracuse UniversityFollow
Niranjan Balasubramanian, Syracuse UniversityFollow
Elizabeth D. Liddy, Syracuse UniversityFollow

Document Type

Conference Document

Date

2005

Keywords

Document classification, Bag-of-words, Natural Language Processing, Support Vector Machine, One-Class algorithm

Language

English

Disciplines

Library and Information Science

Description/Abstract

Experiments were conducted to test several hypotheses on methods for improving document classification for the malicious insider threat problem within the Intelligence Community. Bag-of-words (BOW) representations of documents were compared to Natural Language Processing (NLP) based representations in both the typical and one-class classification problems using the Support Vector Machine algorithm. Results show that the NLP features significantly improved classifier performance over the BOW approach both in terms of precision and recall, while using many fewer features. The one-class algorithm using NLP features demonstrated robustness when tested on new domains.

Recommended Citation

Yilmazel, Ozgur; Symonenko, Svetlana; Balasubramanian, Niranjan; and Liddy, Elizabeth D., "Leveraging One-Class SVM and Semantic Analysis to Detect Anomalous Content" (2005). School of Information Studies - Faculty Scholarship. 54.
https://surface.syr.edu/istpub/54

Creative Commons License

This work is licensed under a Creative Commons Attribution 3.0 License.