Randall S. Sexton

Employee turnover

In todays working environment, a companys human resources are truly the only sustainable competitive advantage. Product innovations can be duplicated, but the synergy of a companys workforce cannot be replicated. It is for this reason that not only attracting talented employees but also retaining them is imperative for success. The study of employee turnover has attempted to explain why employees leave and how to prevent the drain of employee talent. This paper focuses on using a neural network (NN) to predict turnover. If turnover can be found to be predictable the identification of at-risk employees will allow us to focus on their specific needs or concerns in order to retain them in the workforce. Also, by using a Modified Genetic Algorithm to train the NN we can also identify relevant predictors or inputs, which can give us information about how we can improve the work environment as a whole. This research found that a NNSOA trained NN in a 10-fold cross validation experimental design can predict with a high degree of accuracy the turnover rate for a small mid-west manufacturing company.

Toward global optimization of neural networks: a comparison of the genetic algorithm and backpropagation

Abstract The recent surge in activity of neural network research in business is not surprising since the underlying functions controlling business data are generally unknown and the neural network offers a tool that can approximate the unknown function to any degree of desired accuracy. The vast majority of these studies rely on a gradient algorithm, typically a variation of backpropagation, to obtain the parameters (weights) of the model. The well-known limitations of gradient search techniques applied to complex nonlinear optimization problems such as artificial neural networks have often resulted in inconsistent and unpredictable performance. Many researchers have attempted to address the problems associated with the training algorithm by imposing constraints on the search space or by restructuring the architecture of the neural network. In this paper we demonstrate that such constraints and restructuring are unnecessary if a sufficiently complex initial architecture and an appropriate global search algorithm is used. We further show that the genetic algorithm cannot only serve as a global search algorithm but by appropriately defining the objective function it can simultaneously achieve a parsimonious architecture. The value of using the genetic algorithm over backpropagation for neural network optimization is illustrated through a Monte Carlo study which compares each algorithm on in-sample, interpolation, and extrapolation data for seven test functions.

Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing

The escalation of Neural Network research in Business has been brought about by the ability of neural networks, as a tool, to closely approximate unknown functions to any degree of desired accuracy. Although, gradient based search techniques such as back-propagation are currently the most widely used optimization techniques for training neural networks, it has been shown that these gradient techniques are severely limited in their ability to find global solutions. Global search techniques have been identified as a potential solution to this problem. In this paper we examine two well known global search techniques, Simulated Annealing and the Genetic Algorithm, and compare their performance. A Monte Carlo study was conducted in order to test the appropriateness of these global search techniques for optimizing neural networks.

Comparing backpropagation with a genetic algorithm for neural network training

This article shows that the use of a genetic algorithm can provide better results for training a feedforward neural network than the traditional techniques of backpropagation. Using a chaotic time series as an illustration, we directly compare the genetic algorithm and backpropagation for effectiveness, ease-of-use, and efficiency for training neural networks.

Comparative evaluation of genetic algorithm and backpropagation for training neural networks

Abstract In view of several limitations of gradient search techniques (e.g. backpropagation), global search techniques, including evolutionary programming and genetic algorithms (GAs), have been proposed for training neural networks (NNs). However, the effectiveness, ease-of-use, and efficiency of these global search techniques have not been compared extensively with gradient search techniques. Using five chaotic time series functions, this paper empirically compares a genetic algorithm with backpropagation for training NNs. The chaotic series are interesting because of their similarity to economic and financial series found in financial markets.

Reliable classification using neural networks: a genetic algorithm and backpropagation comparison

Abstract Although, the genetic algorithm (GA) has been shown to be a superior neural network (NN) training method on computer-generated problems, its performance — on real world classification data sets is untested. To gain confidence that this alternative training technique is suitable for classification problems, a collection of 10 benchmark real world data sets were used in an extensive Monte Carlo study that compares backpropagation (BP) with the GA for NN training. We find that the GA reliably outperforms the commonly used BP algorithm as an alternative NN training technique. While this does not prove that the GA will always dominate BP, this demonstrated reliability with real world problems enables managers to use NNs trained with GAs as decision support tools with a greater degree of confidence.

Predicting Internet/e-commerce use

Use of the Internet continues to grow at an explosive rate. While entertainment, education and communication serve as important applications of the Internet, e‐commerce continues to emerge as an increasingly significant business phenomenon. However, little empirical research exists to identify factors that influence the extent to which individuals use the Internet and e‐commerce. With the aid of survey research and a neural network, this study analyzes a wide range of variables in an attempt to identify accurate predictors of this usage. The results of the analysis identify gender, overall computer usage, job‐related use, and home access as important characteristics that should influence use of the Internet and e‐commerce.

Machine assessment of neonatal facial expressions of acute pain

We propose that a machine assessment system of neonatal expressions of pain be developed to assist clinicians in diagnosing pain. The facial expressions of 26 neonates (age 18-72h) were photographed experiencing the acute pain of a heel lance and three nonpain stressors. Four algorithms were evaluated on out-of-sample observations: PCA, LDA, SVMs and NNSOA. NNSOA provided the best classification rate of pain versus nonpain (90.20%), followed by SVM with linear kernel (82.35%). We believe these results indicate a high potential for developing a decision support system for diagnosing neonatal pain from images of neonatal facial displays.

Improving Decision Effectiveness of Artificial Neural Networks: A Modified Genetic Algorithm Approach

This study proposes the use of a modified genetic algorithm (MGA), a global search technique, as a training method to improve generalizability and to identify relevant inputs in a neural network (NN) model. Generalizability refers to the NN models ability to perform well on exemplars (observations) that were not used during training (out-of-sample); improved generalizability enhances NNs acceptability as a valid decision-support tool. The MGA improves generalizability by setting unnecessary weights (or connections) to zero and by eliminating these weights. Because the eliminated weights have no further impact on the training (in-sample or out-of-sample data), the relevant variables can be identified from the model. By eliminating unnecessary weights, the MGA is able to search and find a parsimonious model that generalizes well. Unlike the traditional NN, the MGA identifies the model variables that contribute to an outcome, helping decision makers to rationalize output and accept results with greater confidence. The study uses real-life data to demonstrate the use of MGA.

Introduction to Neonatal Facial Pain Detection Using Common and Advanced Face Classification Techniques

Assessing pain in neonates is a challenging problem. Neonates cannot describe their pain experiences but must rely exclusively on the judgments of others. Studies demonstrate, however, that proper diagnosis of pain is impeded by observer bias. It has therefore been recommended that neonatal pain assessment instruments include evaluations that have bypassed an observer. In this article, we describe the Infant COPE project and our work using face classification to detect pain in a neonate’s facial displays. We begin by providing an introduction to face classification that includes an outline of some common and advanced algorithms. We then describe a small database we designed specifically to investigate classifier performance in this problem domain. This is followed by a summary of the experiments we have performed to date, including some preliminary results of current work. We believe these results indicate that the application of face classification to the problem of neonatal pain assessment is a promising area of investigation.