machine learning methods have previously been used for the early detection of heart diseases. However, existing data …-driven machine learning (ML) approaches fall short of providing efficient and accurate heart disease detection. This misdiagnosis … attacks or other heart-related complications. A robust Stacking Ensemble Learner (SEL) is developed using ensemble learning to …

Predictive power has always been the main research focus of learning algorithms with the goal of minimizing the test … on sparse learning in order to avoid overfitting which can greatly affect the accuracy of out-of-sample prediction … response whereas even sparse-learning methods can still preserve a high number of attributes in high-dimensional settings and …

implementation of a forecasting process at the Bundesbank, we benchmark statistical and machine learning methods on demand and supply … retailers. Forecasting the demand for cash on a granular level is crucial in the process to keep logistics costs low, while … models to define banknote production for the coming years, our contribution is to combine features of macro level forecasting …

In this paper we survey the most recent advances in supervised machine learning and highdimensional models for time … series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods we pay special attention to … predictive ability are brie y reviewed. Finally, we discuss application of machine learning in economics and finance and provide …