Exploration of Feature Selection and Advanced Classification Models for High-Stakes Deception Detection
2014 47th Hawaii International Conference on System Sciences (2008)
Waikoloa, Big Island, Hawaii
Jan. 7, 2008 to Jan. 10, 2008
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.
David P. Biros, Dursun Delen, Christie M. Fuller, "Exploration of Feature Selection and Advanced Classification Models for High-Stakes Deception Detection", 2014 47th Hawaii International Conference on System Sciences, vol. 00, no. , pp. 80, 2008, doi:10.1109/HICSS.2008.158