Exploration of feature selection and advanced classification models for high-stakes deception detection

Christie M. Fuller, David P. Biros, Dursun Delen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Recent research has demonstrated the effectiveness of automated text-based deception detection. In this study, using a variety of data sets and common classification techniques, this has been shown to be an accurate technique. Previous results have shown the need to reduce the number of inputs to these models in order to prevent overfitting. While previous results have been promising, there is a need to improve accuracy and reduce the number of false positives. Using 5 classification models and 3 variable sets, we have achieved accuracy level of 76% in this study.

Original languageEnglish
Title of host publicationProceedings of the 41st Annual Hawaii International Conference on System Sciences 2008, HICSS
DOIs
StatePublished - 16 Sep 2008
Externally publishedYes
Event41st Annual Hawaii International Conference on System Sciences 2008, HICSS - Big Island, HI, United States
Duration: 7 Jan 200810 Jan 2008

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
ISSN (Print)1530-1605

Conference

Conference41st Annual Hawaii International Conference on System Sciences 2008, HICSS
CountryUnited States
CityBig Island, HI
Period7/01/0810/01/08

Fingerprint Dive into the research topics of 'Exploration of feature selection and advanced classification models for high-stakes deception detection'. Together they form a unique fingerprint.

  • Cite this

    Fuller, C. M., Biros, D. P., & Delen, D. (2008). Exploration of feature selection and advanced classification models for high-stakes deception detection. In Proceedings of the 41st Annual Hawaii International Conference on System Sciences 2008, HICSS [4438783] (Proceedings of the Annual Hawaii International Conference on System Sciences). https://doi.org/10.1109/HICSS.2008.158