The application of statistical and artificial intelligence (AI) tools in building energy prediction
(BEP) is considered one of the most effective advances toward improving energy efficiency …

Healthcare buildings are characterized by complex energy systems and high energy usage,
therefore serving as the key areas for achieving energy conservation goals in the building …

Ensembles of classifiers have been receiving much attention lately, they consist of a
collection of classifiers that process the same information and their output is combined in …

Boosting algorithms are frequently used in applied data science and in research. To date,
the distinction between boosting with either gradient descent or second-order Newton …

Learning from a multi-class problem has not been an easy task for most of the classifiers,
because of multiple issues. In the complex multi-class scenarios, samples of different …

In this era of big data, processing large scale data efficiently and accurately has become a
challenging problem. Ensemble classification is a type of supervised learning that uses …

Deep learning (DL) classification has become a major research topic in the areas of cancer
prediction, image cell classification, and image classification in medicine. Furthermore, DL …

Bagging, as a commonly-used class imbalance learning method, combines resampling
techniques with ensemble learning to provide a strong classifier with high generalization for …

This paper presents a novel ensemble learning framework inspired by modern portfolio
optimization to address regression problems. This formulation in the ensemble learning field …

Arson is one of the most common crimes, and it has the characteristics of low cost and great
harm. In addition to causing casualties and property damage, arson can often have huge …