stochastic/Probabilistic/fuzzy/dynamic modeling
Ali Sahleh; Maziar Salahi; Sadegh Eskandari
Abstract
Purpose: The aim of this paper is to present an enhanced variant of Twin Parametric-Margin Support Vector Machine (TPMSVM) that improves classification performance.Methodology: By replacing a variable in the objective function, we keep the samples of one class farther from the parametric margin hyperplane ...
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Purpose: The aim of this paper is to present an enhanced variant of Twin Parametric-Margin Support Vector Machine (TPMSVM) that improves classification performance.Methodology: By replacing a variable in the objective function, we keep the samples of one class farther from the parametric margin hyperplane of the other class.Findings: The enhanced model is convex for both linear and nonlinear cases. Also, numerical experiments on UCI datasets show that the enhanced model performs better compared to two similar models for both linear and nonlinear cases.Originality/Value: The previous studies of TPMSVM that increased the accuracy through approaches such as assigning weights to data sample, converting it into an unconstrained model and adding a new term in the objective function, did not guarantee that all samples will be far and on the negative side of the margin hyperplane. However, this study provides an approach to overcome this disadvantage of TPMSVM.
Management and operational budgeting
Malihe Niksirat; Seyed Hadi Nasseri
Abstract
Corona is currently the world's health crisis and the biggest challenge humans have experienced since World War II. Given the epidemic of the disease, it is invaluable to forecasting the number of cases and the resulting deaths to better understand the current situation and provide a short-term plan ...
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Corona is currently the world's health crisis and the biggest challenge humans have experienced since World War II. Given the epidemic of the disease, it is invaluable to forecasting the number of cases and the resulting deaths to better understand the current situation and provide a short-term plan by managers. Accordingly, in this paper, a neuro-fuzzy network model is proposed to forecast the number of cases and deaths in countries that are most affected by this disease. The performance of the proposed neuro-fuzzy network has been compared with time series forecasting neural network as well as radial basic functions neural networks. The proposed model is able to predict the number of cases and deaths from the disease for a period of the next 15 days at a lower error rate.