Abstract Multi-Label Classification (MLC) is an extension of the standard single-label
classification where each data instance is associated with several labels simultaneously …

Machine learning algorithms are increasingly used in various remote sensing applications
due to their ability to identify nonlinear correlations. Ensemble algorithms have been …

The cementitious composites have different properties in the changing environment. Thus,
knowing their mechanical properties is very important for safety reasons. The most important …

We formally study how ensemble of deep learning models can improve test accuracy, and
how the superior performance of ensemble can be distilled into a single model using …

Machine learning systems are both complex and unique. Complex because they consist of
many different components and involve many different stakeholders. Unique because …

Recent advances in the domain of software defect prediction (SDP) include the integration of
multiple classification techniques to create an ensemble or hybrid approach. This technique …

Ensembles, especially ensembles of decision trees, are one of the most popular and
successful techniques in machine learning. Recently, the number of ensemble-based …

Despite significant successes achieved in knowledge discovery, traditional machine
learning methods may fail to obtain satisfactory performances when dealing with complex …

Since canonical machine learning algorithms assume that the dataset has equal number of
samples in each class, binary classification became a very challenging task to discriminate …

In machine learning, the data imbalance imposes challenges to perform data analytics in
almost all areas of real-world research. The raw primary data often suffers from the skewed …