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Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 1
Big Island, Hawaii
January 03-January 06
ISBN: 0-7695-2268-8
Lina Zhou, UMBC, Baltimore, MD
Azene Zenebe, UMBC, Baltimore, MD
Deception detection (DD) is infused with uncertainty due to vagueness and imprecision. To address the above issue, we developed a Model of Uncertainty in Deception Detection (MUDD) and selected the Neuro-Fuzzy classifier to predict deception. A Neuro-fuzzy model integrates the fuzzy set and logic for handling uncertainty with artificial neural network for learning DD models from the data. The performance of the models was empirically tested with deception data collected from synchronous computer-mediated communication. The results show that the performance of the Neuro-fuzzy model is comparable to that of the best model from the traditional machine learning paradigm. Moreover, they have better interpretability, stability, and reliability. We can draw significant theoretical, mathematical, and practical implications to the deception research from this study.
Citation:
Lina Zhou, Azene Zenebe, "Modeling and Handling Uncertainty in Deception Detection," hicss, vol. 1, pp.23a, Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 1, 2005
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