Real-world problems are commonly characterized by a high feature dimensionality, which
hinders the modelling and descriptive analysis of the data. However, some of these data …

Random forest is one of the most widely used machine learning algorithms. Decision trees
used to construct the random forest may have low classification accuracies or high …

The intensive research and development efforts directed towards large-scale complex
industrial systems in the context of Industry 4.0 indicate that safety and reliability issues pose …

Feature selection approaches aim to select a set of prominent features that best describe the
data to improve the efficiency without degrading the performance of the model. In many real …

Traditional feature selection methods assume that all data instances and features are known
before learning. However, it is not the case in many real-world applications that we are more …

Stacking and bagging are widely used ensemble learning approaches that make use of
multiple classifier systems. Stacking focuses on building an ensemble of heterogeneous …

In the medical field, distinguishing genes that are relevant to a specific disease, let's say
colon cancer, is crucial to finding a cure and understanding its causes and subsequent …

Osteosarcoma is the most common bone malignancy, with the highest incidence in children
and adolescents. Survival rate prediction is important for improving prognosis and planning …

Cyber attacks are becoming more sophisticated and, hence, more difficult to detect. Using
efficient and effective machine learning techniques to detect network anomalies and …

Deep learning models are tools for data analysis suitable for approximating (non-linear)
relationships among variables for the best prediction of an outcome. While these models can …