Open AccessA Novel Approach for Lie Detection Based on F-Score and Extreme Learning Machine
Author(s) -
Junfeng Gao,
Zhao Wang,
Yong Yang,
Zhang Wenjia,
Chunyi Tao,
Jiunian Guan,
Nini Rao
Publication year - 2013
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0064704
Subject(s) - extreme learning machine , artificial intelligence , computer science , classifier (uml) , support vector machine , pattern recognition (psychology) , principal component analysis , feature selection , machine learning , hyperparameter optimization , f1 score , cross validation , feature (linguistics) , feature vector , artificial neural network , linguistics , philosophy
A new machine learning method referred to as F-score_ELM was proposed to classify the lying and truth-telling using the electroencephalogram (EEG) signals from 28 guilty and innocent subjects. Thirty-one features were extracted from the probe responses from these subjects. Then, a recently-developed classifier called extreme learning machine (ELM) was combined with F-score, a simple but effective feature selection method, to jointly optimize the number of the hidden nodes of ELM and the feature subset by a grid-searching training procedure. The method was compared to two classification models combining principal component analysis with back-propagation network and support vector machine classifiers. We thoroughly assessed the performance of these classification models including the training and testing time, sensitivity and specificity from the training and testing sets, as well as network size. The experimental results showed that the number of the hidden nodes can be effectively optimized by the proposed method. Also, F-score_ELM obtained the best classification accuracy and required the shortest training and testing time.