Ensemble learning is a prolific field in Machine Learning since it is based on the assumption
that combining the output of multiple models is better than using a single model, and it …

The early diagnosis of cancer, as one of the major causes of death, is vital for cancerous
patients. Diagnosing diseases in general and cancer in particular is a considerable …

Data collection is a major bottleneck in machine learning and an active research topic in
multiple communities. There are largely two reasons data collection has recently become a …

The combined impact of new computing resources and techniques with an increasing
avalanche of large datasets, is transforming many research areas and may lead to …

K-nearest neighbor (KNN) rule is a well-known non-parametric classifier that is widely used
in pattern recognition. However, the sensitivity of the neighborhood size k always seriously …

Learning and mining from imbalanced datasets gained increased interest in recent years.
One simple but efficient way to increase the performance of standard machine learning …

Classification of imbalanced datasets is a challenging task for standard algorithms. Although
many methods exist to address this problem in different ways, generating artificial data for …

Feature selection is a meaningful aspect of data mining that aims to select more relevant
data features and provide more concise and explicit data descriptions. It is beneficial for …

Extending previous work on quantile classifiers (q-classifiers) we propose the q*-classifier
for the class imbalance problem. The classifier assigns a sample to the minority class if the …

Imbalanced data classification remains a focus of intense research, mostly due to the
prevalence of data imbalance in various real-life application domains. A disproportion …