Data Processing of Deception Detection Based on PCA

DOI

10.2991/meici-15.2015.130How to use a DOI?

Keywords

Data processing; Principal component analysis; Deception detection

Abstract

Based on the analysis of the difficulty in data processing parameters caused by too many parameters in traditional deception detection, it is presented in this paper to use the method of principal component analysis (PCA) to reduce the number of parameters and the dimension of variables, thus facilitating data processing. Though specific experiments, it is found that the accuracy rate is 90.5% after using PCA, which is not quite different from that obtained before using this method. Moreover, the parameters highly correlated can be well eliminated with PCA, and the redundant data among variables can also be reduced. On the basis of retaining most data, the problems in original data can be effectively solved by using fewer variables, thus fulfilling the purpose of simplifying data and facilitating the analysis of problems. For deception detection experiments with large amounts of data, this is undoubtedly a feasible approach.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zilong Chen
AU  - Dong Liu
AU  - Zhiwei Gong
AU  - Yunsheng Liu
AU  - Ruyu Li
PY  - 2015/06
DA  - 2015/06
TI  - Data Processing of Deception Detection Based on PCA
BT  - Proceedings of the 2015 International Conference on Management, Education, Information and Control
PB  - Atlantis Press
SP  - 741
EP  - 746
SN  - 1951-6851
UR  - https://doi.org/10.2991/meici-15.2015.130
DO  - 10.2991/meici-15.2015.130
ID  - Chen2015/06
ER  -