Matching Items (2)
Filtering by
- All Subjects: EEG
- All Subjects: matched data
- Creators: Runger, George
Description
Lie detection is used prominently in contemporary society for many purposes such as for pre-employment screenings, granting security clearances, and determining if criminals or potential subjects may or may not be lying, but by no means is not limited to that scope. However, lie detection has been criticized for being subjective, unreliable, inaccurate, and susceptible to deliberate manipulation. Furthermore, critics also believe that the administrator of the test also influences the outcome as well. As a result, the polygraph machine, the contemporary device used for lie detection, has come under scrutiny when used as evidence in the courts. The purpose of this study is to use three entirely different tools and concepts to determine whether eye tracking systems, electroencephalogram (EEG), and Facial Expression Emotion Analysis (FACET) are reliable tools for lie detection. This study found that certain constructs such as where the left eye is looking at in regard to its usual position and engagement levels in eye tracking and EEG respectively could distinguish between truths and lies. However, the FACET proved the most reliable tool out of the three by providing not just one distinguishing variable but seven, all related to emotions derived from movements in the facial muscles during the present study. The emotions associated with the FACET that were documented to possess the ability to distinguish between truthful and lying responses were joy, anger, fear, confusion, and frustration. In addition, an overall measure of the subject's neutral and positive emotional expression were found to be distinctive factors. The implications of this study and future directions are discussed.
ContributorsSeto, Raymond Hua (Author) / Atkinson, Robert (Thesis director) / Runger, George (Committee member) / W. P. Carey School of Business (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Matching or stratification is commonly used in observational studies to remove bias due to confounding variables. Analyzing matched data sets requires specific methods which handle dependency among observations within a stratum. Also, modern studies often include hundreds or thousands of variables. Traditional methods for matched data sets are challenged in high-dimensional settings, mixed type variables (numerical and categorical), nonlinear andinteraction effects. Furthermore, machine learning research for such structured data is quite limited. This dissertation addresses this important gap and proposes machine learning models for identifying informative variables from high-dimensional matched data sets. The first part of this dissertation proposes a machine learning model to identify informative variables from high-dimensional matched case-control data sets. The outcome of interest in this study design is binary (case or control), and each stratum is assumed to have one unit from each outcome level. The proposed method which is referred to as Matched Forest (MF) is effective for large number of variables and identifying interaction effects. The second part of this dissertation proposes three enhancements of MF algorithm. First, a regularization framework is proposed to improve variable selection performance in excessively high-dimensional settings. Second, a classification method is proposed to classify unlabeled pairs of data. Third, two metrics are proposed to estimate the effects of important variables identified by MF. The third part proposes a machine learning model based on Neural Networks to identify important variables from a more generalized matched case-control data set where each stratum has one unit from case outcome level and more than one unit from control outcome level. This method which is referred to as Matched Neural Network (MNN) performs better than current algorithms to identify variables with interaction effects. Lastly, a generalized machine learning model is proposed to identify informative variables from high-dimensional matched data sets where the outcome has more than two levels. This method outperforms existing algorithms in the literature in identifying variables with complex nonlinear and interaction effects.
ContributorsShomal Zadeh, Nooshin (Author) / Runger, George (Thesis advisor) / Montgomery, Douglas (Committee member) / Shinde, Shilpa (Committee member) / Escobedo, Adolfo (Committee member) / Arizona State University (Publisher)
Created2021