P300-based deception detection of mock network fraud with modified genetic algorithm and combined classification

To detect network fraud, a three-stimulus paradigm was used in a mock crime P300-based concealed information test. A P300-based deception detection method based on a modified genetic algorithm and a confidence-coefficient-based combined classifier was created for mock network fraud detection. After...

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Bibliographic Details
Published in:The ... International Winter Conference on Brain-Computer Interface pp. 1 - 4
Main Authors: Liu, Xiaochen, Shen, Jizhong, Zhao, Wufeng
Format: Conference Proceeding
Language:English
Published: IEEE 01.02.2019
Subjects:
combined classifier
confidence coefficient
Electroencephalography
Feature extraction
Genetic algorithms
improved multi-population genetic algorithm
Logistics
mock crime
network fraud
P300
Signal processing
Sociology
Statistics
ISSN:2572-7672
Online Access:Get full text
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Summary:To detect network fraud, a three-stimulus paradigm was used in a mock crime P300-based concealed information test. A P300-based deception detection method based on a modified genetic algorithm and a confidence-coefficient-based combined classifier was created for mock network fraud detection. After the multi-domain integrated signal preprocessing and feature extraction, a modified logistic equation based multi-population genetic algorithm was adopted for feature selection to obtain an optimal feature subset. Then the confidence coefficient was proposed to determine the classification difficulty levels of samples. A combined classifier based on confidence coefficient was proposed for classification. Compared with the component classifiers and other individual classifiers, the combined classifier requires 34% less computing time and the mean classification accuracy rate is 0.2 to 2.23 percentage points higher for twelve subjects using leave-one-out cross validation. Experiment results confirm that the proposed method is effective to detect deception during network fraud simulation.
ISSN:2572-7672
DOI:10.1109/IWW-BCI.2019.8737320