An exploratory study on promising cues in deception detection and application of decision tree

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

21 Scopus citations

Abstract

Automatic deception detection (ADD) becomes more and more important. ADD can be facilitated with the development of data mining techniques. In the paper we focus on decision tree to automatic classify deceptions. The major question is how to select experiment data (input data for training in decision tree) so that it maximally benefits the decision tree performance. We investigate promising level of the cues of experiment data, and then adjust the applications in decision tree accordingly. Five comparative decision tree experiments demonstrate that tree performance, such as accurate rate and complexity, is dramatically improved by statistically and semantically selecting cues.

Original languageEnglish (US)
Title of host publicationProceedings of the Hawaii International Conference on System Sciences
EditorsR.H. Sprague Jr.
Pages357-366
Number of pages10
Volume37
Publication statusPublished - 2004
EventProceedings of the Hawaii International Conference on System Sciences - Big Island, HI., United States
Duration: Jan 5 2004Jan 8 2004

Other

OtherProceedings of the Hawaii International Conference on System Sciences
CountryUnited States
CityBig Island, HI.
Period1/5/041/8/04

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ASJC Scopus subject areas

  • Software
  • Industrial and Manufacturing Engineering

Cite this

Qin, T., Burgoon, J. K., & Nunamaker, J. F. (2004). An exploratory study on promising cues in deception detection and application of decision tree. In R. H. Sprague Jr. (Ed.), Proceedings of the Hawaii International Conference on System Sciences (Vol. 37, pp. 357-366). [CLDDN05]