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Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008) (2008)
Waikoloa, Big Island, Hawaii
Jan. 7, 2008 to Jan. 10, 2008
ISSN: 1530-1605
ISBN: 0-7695-3075-3
pp: 80
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.
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CITATION

D. P. Biros, D. Delen and C. M. Fuller, "Exploration of Feature Selection and Advanced Classification Models for High-Stakes Deception Detection," Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008)(HICSS), Waikoloa, Big Island, Hawaii, 2008, pp. 80.
doi:10.1109/HICSS.2008.158
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