In classification problems, the occurrence of abnormal observations is often encountered. How to obtain a stable model deal with outliers has always been subject widespread concern. this article, we draw on ideas AdaBoosting algorithm and propose asymptotically linear loss function, which makes output function more for contaminated samples, two boosting algorithms were designed based different ...

Machine learning has increasingly found its way into the credit risk literature. When applied to forecasting parameters, approaches have been outperform standard statistical models. The quantification of prediction uncertainty is typically not analyzed in machine setting. However, this vital interests managers and regulators alike as increases transparency stability management reporting tasks. ...

Stock market forecasting has attracted so many researchers and investors that ‎many studies have been done in this field. These studies have led to the ‎development of many predictive methods, the most widely used of which are ‎machine learning-based methods. In machine learning-based methods, loss ‎function has a key role in determining the model weights. In this study a new loss ‎function is ...