TY - GEN
T1 - Exploration of feature selection and advanced classification models for high-stakes deception detection
AU - Fuller, Christie M.
AU - Biros, David P.
AU - Delen, Dursun
PY - 2008/9/16
Y1 - 2008/9/16
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=51449112010&partnerID=8YFLogxK
U2 - 10.1109/HICSS.2008.158
DO - 10.1109/HICSS.2008.158
M3 - Conference contribution
AN - SCOPUS:51449112010
SN - 0769530753
SN - 9780769530758
T3 - Proceedings of the Annual Hawaii International Conference on System Sciences
BT - Proceedings of the 41st Annual Hawaii International Conference on System Sciences 2008, HICSS
T2 - 41st Annual Hawaii International Conference on System Sciences 2008, HICSS
Y2 - 7 January 2008 through 10 January 2008
ER -