Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …

Ensemble methods are considered the state‐of‐the art solution for many machine learning
challenges. Such methods improve the predictive performance of a single model by training …

We propose a novel high-performance and interpretable canonical deep tabular data
learning architecture, TabNet. TabNet uses sequential attention to choose which features to …

Electricity load forecasting has been attracting research and industry attention because of its
importance for energy management, infrastructure planning, and budgeting. In recent years …

River is a machine learning library for dynamic data streams and continual learning. It
provides multiple state-of-the-art learning methods, data generators/transformers …

In many applications of information systems learning algorithms have to act in dynamic
environments where data are collected in the form of transient data streams. Compared to …

scikit-multiflow is a framework for learning from data streams and multi-output learning in
Python. Conceived to serve as a platform to encourage the democratization of stream …

Random forests is currently one of the most used machine learning algorithms in the non-
streaming (batch) setting. This preference is attributable to its high learning performance and …

Incremental learning, online learning, and data stream learning are terms commonly
associated with learning algorithms that update their models given a continuous influx of …

Ensemble-based methods are among the most widely used techniques for data stream
classification. Their popularity is attributable to their good performance in comparison to …