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- Novel Regression and Least Square Support Vector Machine Learning Technique for Air Pollution Forecasting(arXiv)
Author : : Dhanalakshmi M, Radha V
Abstract : Air pollution is the origination of particulate matter, chemicals, or biological substances that brings pain to either humans or other living creatures or instigates discomfort to the natural habitat and the airspace. Hence, air pollution remains one of the paramount environmental issues as far as metropolitan cities are concerned. Several air pollution benchmarks are even said to have a negative influence on human health. Also, improper detection of air pollution benchmarks results in severe complications for humans and living creatures. To address this aspect, a novel technique called, Discretized Regression and Least Square Support Vector (DR-LSSV) based air pollution forecasting is proposed. The results indicate that the proposed DR-LSSV Technique can efficiently enhance air pollution forecasting performance and outperforms the conventional machine learning methods in terms of air pollution forecasting accuracy, air pollution forecasting time, and false positive rate
2. A Robust Optimization Model for Nonlinear Support Vector Machine(arXiv)
Author : Francesca Maggioni, Andrea Spinelli
Abstract : In this paper we present new optimization models for Support Vector Machine (SVM), with the aim of separating data points in two classes by means of a nonlinear classifier. Traditionally, in the nonlinear context data points are firstly mapped to a higher dimensional space and then classified through a SVM-type model. In order to increase the predictive power of SVM, within our approach we include a final linear search procedure aiming to minimize the overall number of misclassified points. Along with a deterministic model in which data are assumed to be perfectly known, we formulate a robust optimization model with bounded-by-lp-norm uncertainty sets. Indeed, when data are real-world observations, measurement errors or noise may corrupt the quality of input values. For this reason, facing uncertainty in the model is a way to robustify the approach. All formulations reduce to linear models with advantages in terms of efficiency compared to other approaches in the literature. Extensive numerical results on real-world datasets show the benefits in terms of accuracy when considering nonlinear decision classifier and protecting the model against uncertainties. Finally, managerial insights to guide the final user are provided.