ROC-based model estimation for forecasting large changes in demand

Schneider, Matthew J.; Gorr, Wilpen L.

doi:10.1016/j.ijforecast.2014.07.002

Cited by 3 publications

(1 citation statement)

References 27 publications

(33 reference statements)

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“…Receiver operating characteristic (ROC) curves are such a general approach to evaluate the predictive power of estimated recession probabilities and, more general, of any leading indicator of the business cycle. While ROC curves are often used in the machine-learning literature to study the predictive power of regression and classification techniques (see, for example, the introductory textbook by James et al, 2013) they have become popular in economics only recently (Berge and Jordà, 2011;Lahiri and Wang, 2013;Bluedorn et al, 2013;Liu and Moench, 2014;Savona and Vezzoli, 2015;Schneider and Gorr, 2015), so that we briefly describe how to construct an ROC curve.…”

Section: Evaluation Of Forecastsmentioning

confidence: 99%

Predicting recessions with boosted regression trees

Döpke

Fritsche

Pierdzioch

2017

International Journal of Forecasting

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Section: Evaluation Of Forecastsmentioning

confidence: 99%

Predicting recessions with boosted regression trees

Döpke

Fritsche

Pierdzioch

2017

International Journal of Forecasting

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Support Vector Algorithms for Optimizing the Partial Area under the ROC Curve

Narasimhan

Agarwal

2017

Neural Computation

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The area under the ROC curve (AUC) is a widely used performance measure in machine learning. Increasingly, however, in several applications, ranging from ranking to biometric screening to medicine, performance is measured not in terms of the full area under the ROC curve, but in terms of the partial area under the ROC curve between two false positive rates. In this paper, we develop support vector algorithms for directly optimizing the partial AUC between any two false positive rates. Our methods are based on minimizing a suitable proxy or surrogate objective for the partial AUC error. In the case of the full AUC, one can readily construct and optimize convex surrogates by expressing the performance measure as a summation of pairwise terms. The partial AUC, on the other hand, does not admit such a simple decomposable structure, making it more challenging to design and optimize (tight) convex surrogates for this measure.Our approach builds on the structural SVM framework of Joachims (2005) to design convex surrogates for partial AUC, and solves the resulting optimization problem using a cutting plane solver. Unlike the full AUC, where the combinatorial optimization needed in each iteration of the cutting plane solver can be decomposed and solved efficiently, the corresponding problem for the partial AUC is harder to decompose. One of our main contributions is a polynomial time algorithm for solving the combinatorial optimization problem associated with partial AUC. We also develop an approach for optimizing a tighter non-convex hinge loss based surrogate for the partial AUC using difference-of-convex programming. Our experiments on a variety of real-world and benchmark tasks confirm the efficacy of the proposed methods.

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Comparative Study of Convolutional Neural Network and Conventional Machine Learning Methods for Landslide Susceptibility Mapping

Liu

Yang

et al. 2022

Remote Sensing

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Landslide susceptibility mapping (LSM) is a useful tool to estimate the probability of landslide occurrence, providing a scientific basis for natural hazards prevention, land use planning, and economic development in landslide-prone areas. To date, a large number of machine learning methods have been applied to LSM, and recently the advanced convolutional neural network (CNN) has been gradually adopted to enhance the prediction accuracy of LSM. The objective of this study is to introduce a CNN-based model in LSM and systematically compare its overall performance with the conventional machine learning models of random forest, logistic regression, and support vector machine. Herein, we selected Zhangzha Town in Sichuan Province, China, and Lantau Island in Hong Kong, China, as the study areas. Each landslide inventory and corresponding predisposing factors were stacked to form spatial datasets for LSM. The receiver operating characteristic analysis, area under the curve (AUC), and several statistical metrics, such as accuracy, root mean square error, Kappa coefficient, sensitivity, and specificity, were used to evaluate the performance of the models. Finally, the trained models were calculated, and the landslide susceptibility zones were mapped. Results suggest that both CNN and conventional machine learning-based models have a satisfactory performance. The CNN-based model exhibits an excellent prediction capability and achieves the highest performance but also significantly reduces the salt-of-pepper effect, which indicates its great potential for application to LSM.

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ROC-based model estimation for forecasting large changes in demand

Cited by 3 publications

References 27 publications

Predicting recessions with boosted regression trees

Predicting recessions with boosted regression trees

Support Vector Algorithms for Optimizing the Partial Area under the ROC Curve

Comparative Study of Convolutional Neural Network and Conventional Machine Learning Methods for Landslide Susceptibility Mapping

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