Gradient Boosting is a widely-used machine learning technique that has proven highly
effective in batch learning. However, its effectiveness in stream learning contexts lags …

This paper describes the implementation of a prediction model for real-time assessment of
weather related outages in the electric transmission system. The network data and historical …

Machine learning (ML) models for analyzing medical data are critical for both accelerating
development of novel diagnostic and treatment strategies and improving the accuracy of …

Attaining the proper balance between underfitting and overfitting is one of the central
challenges in machine learning. It has been approached mostly by deriving bounds on …

The exponential growth of the internet has resulted in an overwhelming influx of big data.
However, traditional batch learning models face significant obstacles in effectively learning …

The primary goal of Machine learning (ML) models in the prediction of medical conditions is
to accurately predict (classify) the occurrence of a disease, or therapy. Many ML models …

A plethora of high-impact applications involve predictive modeling of structured data. In
various domains, from hospital readmission prediction in the medical realm, though weather …

Abstract The Big Data revolution brought an increasing availability of data sets of
unprecedented scales, enabling researchers in machine learning and data mining …

Quantifying the properties of interest is an important problem in many domains, eg,
assessing the condition of a patient, estimating the risk of an investment or relevance of the …