Machine learning has shown utility in detecting patterns within large, unstructured, and
complex datasets. One of the promising applications of machine learning is in precision …

This paper offers a comprehensive approach to feature selection in the scope of
classification problems, explaining the foundations, real application problems and the …

In the last decade, ensemble learning has become a prolific discipline in pattern recognition,
based on the assumption that the combination of the output of several models obtains better …

Enterprise credit risk prediction in the supply chain context is an important step for decision
making and early credit crisis warnings. Improving the prediction performance of this task is …

Ensemble classification is a well-established approach that involves fusing the decisions of
multiple predictive models. A similar “ensemble logic” has been recently applied to …

Feature selection ensemble methods are a recent approach aiming at adding diversity in
sets of selected features, improving performance and obtaining more robust and stable …

Subject matter experts (SMEs) often rely on attribute importance rankings to verify machine
learning models, acquire insights into their outcomes, and gain a deeper understanding of …

Feature selection has kept playing a significant role in the workflow of materials machine
learning, but currently most of works of materials machine learning tend to use single or …

In recent years, ensemble learning has become a prolific area of study in pattern
recognition, based on the assumption that using and combining different learning models in …

PD (Parkinson's Disease) is a severe malady that is painful and incurable, affecting older
human beings. Identifying PD early in a precise manner is critical for the lengthened survival …