A misclassification cost‐minimizing evolutionary–neural classification approach

Pendharkar, Parag C.; Nanda, Sudhir

doi:10.1002/nav.20154

Cited by 28 publications

(18 citation statements)

References 49 publications

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“…The data for 1987–92 were treated as the training sample, while the data for 1993–95 were the testing sample. The description of our data can be found in our previous study (Pendharkar & Nanda, 2006). To predict bankruptcy, we used financial ratios, which were earnings before interest and taxes/interest expense, earnings before interest and taxes/assets, current assets/current liabilities, retained earnings/assets, and market value of equity/book value of debt.…”

Section: Data Experiments and Resultsmentioning

confidence: 99%

A comparison of gradient ascent, gradient descent and genetic‐algorithm‐based artificial neural networks for the binary classification problem

Pendharkar¹

2007

Expert Systems

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We compare log maximum likelihood gradient ascent, root-mean-square error minimizing gradient descent and genetic-algorithm-based artificial neural network procedures for a binary classification problem. We use simulated data and real-world data sets, and four different performance metrics of correct classification, sensitivity, specificity and reliability for our comparisons. Our experiments indicate that a genetic-algorithmbased artificial neural network that maximizes the total number of correct classifications generally fares well for the binary classification problem. However, if the training data set contains inconsistent decisions or noise then the log maximum likelihood maximizing gradient ascent may be the best classification approach to use. The root-mean-square minimizing gradient descent approach appears to overfit training data and has the lowest reliability among the approaches considered for our research. At the end of the paper, we provide a few guidelines, including computational complexity, for selection of an appropriate technique for a given binary classification problem.

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Section: Data Experiments and Resultsmentioning

confidence: 99%

A comparison of gradient ascent, gradient descent and genetic‐algorithm‐based artificial neural networks for the binary classification problem

Pendharkar¹

2007

Expert Systems

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“…Zheng and Padmanabhan (2006) have recently extended this idea of 'active learning' to the cost-effective acquisition of additional data to enhance classification performance. So far, only a few OR contributions have linked asymmetric costs or imbalanced data sets routinely found in OR applications to the methods and processes of DM (Viaene and Dedene, 2005;Janssens et al, 2006;Pendharkar and Nanda, 2006).…”

Section: Computer-intensive Methodsmentioning

confidence: 99%

Forecasting and operational research: a review

Fildes

Νικολόπουλος

Crone

et al. 2008

Journal of the Operational Research Society

226

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From its foundation, operational research (OR) has made many substantial contributions to practical forecasting in organizations. Equally, researchers in other disciplines have influenced forecasting practice. Since the last survey articles in JORS, forecasting has developed as a discipline with its own journals. While the effect of this increased specialization has been a narrowing of the scope of OR's interest in forecasting, research from an OR perspective remains vigorous. OR has been more receptive than other disciplines to the specialist research published in the forecasting journals, capitalizing on some of their key findings. In this paper, we identify the particular topics of OR interest over the past 25 years. After a brief summary of the current research in forecasting methods, we examine those topic areas that have grabbed the attention of OR researchers: computationally intensive methods and applications in operations and marketing. Applications in operations have proved particularly important, including the management of inventories and the effects of sharing forecast information across the supply chain. The second area of application is marketing, including customer relationship management using data mining and computer-intensive methods. The paper concludes by arguing that the unique contribution that OR can continue to make to forecasting is through developing models that link the effectiveness of new forecasting methods to the organizational context in which the models will be applied. The benefits of examining the system rather than its separate components are likely to be substantial.

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“…Regression tree models are more efficient than genetic programming and neural network models [37]. In a study comparing a regression tree model and neural networks for forecasting software effort, Srinivasan and Fisher found that the former performs as well as the latter.…”

Section: Data Collection and Analysismentioning

confidence: 98%

An exploratory study of object-oriented software component size determinants and the application of regression tree forecasting models

Pendharkar

2004

Information & Management

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A misclassification cost‐minimizing evolutionary–neural classification approach

Cited by 28 publications

References 49 publications

A comparison of gradient ascent, gradient descent and genetic‐algorithm‐based artificial neural networks for the binary classification problem

A comparison of gradient ascent, gradient descent and genetic‐algorithm‐based artificial neural networks for the binary classification problem

Forecasting and operational research: a review

An exploratory study of object-oriented software component size determinants and the application of regression tree forecasting models

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