Improvements in Personnel Selection With Neural Networks: A Pilot Study in the Field of Aviation Psychology
01 Feb 2004-The International Journal of Aviation Psychology (Lawrence Erlbaum Associates, Inc.)-Vol. 14, Iss: 1, pp 103-115
TL;DR: In this paper, the authors discuss problems of data combination in personnel selection and show that neural networks are applicable even when the discriminant analysis produces biased results due to violations of its assumptions, and also outperform the logistic regression analysis in terms of separability of correct and incorrect classifications.
Abstract: This article discusses problems of data combination in personnel selection. In a study on selecting candidates for pilot training, 82 participants were tested. The predictor variables were attention, vigilance, reactivity under stress, and spatial and numerical ability. The evaluation of the applicants after training was used as the criterion. To test the predictive validity of the test battery neural networks, linear discriminant analysis and logistic regression analysis were used. The results indicate that neural networks are applicable even when the discriminant analysis produces biased results due to violations of its assumptions, and also outperform the logistic regression analysis in terms of separability of correct and incorrect classifications based on the height of the classification probability.
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TL;DR: When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests.
Abstract: Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Press' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5. When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing.
331 citations
Cites background from "Improvements in Personnel Selection..."
...More recently, research has been steadily building on the accuracy and efficiency of data mining, with classifiers like Neural Networks (NN), Support Vector Machines (SVM), Classification Trees (CT) and Random Forests (RF) used for medical prediction and classification tasks [13,14,19-27]....
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TL;DR: An algorithm is presented that provides the one-dimensional subspace, where the Bayes error is minimized for the C class problem with homoscedastic Gaussian distributions, and can be used to improve upon the outcomes provided by existing algorithms and derive a low-computational cost, linear approximation.
Abstract: We present an algorithm that provides the one-dimensional subspace, where the Bayes error is minimized for the C class problem with homoscedastic Gaussian distributions. Our main result shows that the set of possible one-dimensional spaces v, for which the order of the projected class means is identical, defines a convex region with associated convex Bayes error function g(v). This allows for the minimization of the error function using standard convex optimization algorithms. Our algorithm is then extended to the minimization of the Bayes error in the more general case of heteroscedastic distributions. This is done by means of an appropriate kernel mapping function. This result is further extended to obtain the d dimensional solution for any given d by iteratively applying our algorithm to the null space of the (d - l)-dimensional solution. We also show how this result can be used to improve upon the outcomes provided by existing algorithms and derive a low-computational cost, linear approximation. Extensive experimental validations are provided to demonstrate the use of these algorithms in classification, data analysis and visualization.
141 citations
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TL;DR: This paper contributes towards understanding the role of artificial intelligence methods for analyzing behavioral data in psychology by investigating the possible links between life satisfaction, self-esteem, anxiety, depression, and the intensity of Instagram use with a social comparison model.
Abstract: Systematic exposure to social media causes social comparisons, especially among women who compare their image to others; they are particularly vulnerable to mood decrease, self-objectification, body concerns, and lower perception of themselves. This study first investigates the possible links between life satisfaction, self-esteem, anxiety, depression, and the intensity of Instagram use with a social comparison model. In the study, 974 women age 18-49 who were Instagram users voluntarily participated, completing a questionnaire. The results suggest associations between the analyzed psychological data and social comparison types. Then, artificial neural networks models were implemented to predict the type of such comparison (positive, negative, equal) based on the aforementioned psychological traits. The models were able to properly predict between 71% and 82% of cases. As human behavior analysis has been a subject of study in various fields of science, this paper contributes towards understanding the role of artificial intelligence methods for analyzing behavioral data in psychology.
14 citations
TL;DR: The best performing neural network type was the multiple layer perception, which showed high sensitivity, the second highest specificity, and high accuracy, while radial basis networks and probabilistic networks both fail to predict correctly the candidates who fail on the flight screening program.
Abstract: We evaluated the predictive classification accuracy of discriminant analysis, logistic regression and four neural network typologies (multiple layer perceptrons, radial basis networks, probabilistic neural networks, and linear neural networks) on a flight screening program with a pass–fail criterion using several psychometric tests as predictors. A stepwise (for logistic regression and discriminant analysis) and sensitivity (for neural networks) selection procedure identified spatial visualization, eye–hand–foot coordination, and concentration capacity as significant predictors. Performance on the first few flights of the screening program was also retained as a significant predictor of final score. Regarding the accuracy of predictions, logistic regression showed the highest accuracy (77%), with high sensitivity (92%) but low specificity (31%). Discriminant analysis had high sensitivity (77%) and high specificity (64%). However, it had the second lowest accuracy (74%). The best performing neural network ...
13 citations
TL;DR: In this paper, a pilot study for the selection of trainee pilots for the German Luftwaffe, 99 applicants were assessed using a comprehensive battery of tests that measured inductive thinking, spatial thinking, attentiveness, visual and verbal short-term memory, sensorimotor coordination, and reactive stress tolerance.
Abstract: This study addresses the issue of data combination in personnel selection. In a pilot study for the selection of trainee pilots for the German Luftwaffe, 99 applicants were assessed using a comprehensive battery of tests that measured inductive thinking, spatial thinking, attentiveness, visual and verbal short-term memory, sensorimotor coordination, and reactive stress tolerance. The global evaluation of the applicants' performance in a flight simulator served as an external criterion. The predictive validity of this test battery was checked by carrying out a discriminant analysis as well as by calculating a neural network. The 2 methods were compared with regard to their classification rate, stability, and separation of correct and incorrect classifications. The results show that artificial neural networks are useful tools for improving the quality of selection procedures for trainee pilots.
10 citations
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01 Jan 1995TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Abstract: From the Publisher:
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.
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TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.
Abstract: Publisher Summary This chapter provides an account of different neural network architectures for pattern recognition. A neural network consists of several simple processing elements called neurons. Each neuron is connected to some other neurons and possibly to the input nodes. Neural networks provide a simple computing paradigm to perform complex recognition tasks in real time. The chapter categorizes neural networks into three types: single-layer networks, multilayer feedforward networks, and feedback networks. It discusses the gradient descent and the relaxation method as the two underlying mathematical themes for deriving learning algorithms. A lot of research activity is centered on learning algorithms because of their fundamental importance in neural networks. The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue. It closes with the discussion of performance and implementation issues.
13,033 citations
"Improvements in Personnel Selection..." refers background or methods in this paper
...…and stability of the obtained results in case there is no second independent data set available to carry out more extensive outsample analysis (Bishop, 1995; Dorffner, 1991; Michie, Spiegelhalter, & Taylor, 1994) and is even used commonly in psychological studies (e.g., Hagemeister, Scholz, &…...
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...According to the literature on neural networks, the classification rates of classical statistical methods and neural network algorithms are comparable in such cases (Bishop, 1995)....
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...This method is a widely used technique to estimate the generalizability and stability of the obtained results in case there is no second independent data set available to carry out more extensive outsample analysis (Bishop, 1995; Dorffner, 1991; Michie, Spiegelhalter, & Taylor, 1994) and is even used commonly in psychological studies (e....
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TL;DR: The authors may not be able to make you love reading, but neural networks a systematic introduction will lead you to love reading starting from now.
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"Improvements in Personnel Selection..." refers background in this paper
...…and is a robust procedure for pattern recognition, which learns to assign participants to predefined categories based on their predictor variables, using specific classification algorithms (Dorffner, 1991; Kinnebrock, 1992; Mielke, 2001; Rojas, 1993, 2000; Spies, 1993; Warner & Misra, 1996)....
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TL;DR: In this article, the authors examined the relation between two or more actions that were assumed to reflect the same underlying disposition, and provided little evidence to support the postulated existence of stable, underlying attitudes within the individual, which influence both verbal expressions and actions.
Abstract: Publisher Summary In the domain of personality psychology, the trait concept has carried the burden of dispositional explanation. A multitude of personality traits has been identified and new trait dimensions continue to join the growing list. In a similar fashion, the concept of attitude has been the focus of attention in the explanations of human behavior offered by social psychologists. Numerous attitudes have been assessed over the years and, as new social issues emerge, additional attitudinal domains are explored. The chapter provides little evidence to support the postulated existence of stable, underlying attitudes within the individual, which influence both verbal expressions and actions. It examines the relation between two or more actions that were assumed to reflect the same underlying disposition. The aggregation of responses across time, contexts, targets, or actions or across a combination of these elements permits the inferences of dispositions at varying levels of generality.
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"Improvements in Personnel Selection..." refers background in this paper
...Wittmann and Süß (1997) and Ajzen (1987) mentioned that for more general and global criteria, aggregate measures such as the general ability are better suited for prediction than more specific predictors....
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