Not all data in a typical training set help with generalization; some samples can be overly
ambiguous or outrightly mislabeled. This paper introduces a new method to identify such …

Label noise is an important issue in classification, with many potential negative
consequences. For example, the accuracy of predictions may decrease, whereas the …

Classification datasets often have an unequal class distribution among their examples. This
problem is known as imbalanced classification. The Synthetic Minority Over-sampling …

Many real-world applications reveal difficulties in learning classifiers from imbalanced data.
The rising big data era has been witnessing more classification tasks with large-scale but …

Many real-world applications reveal difficulties in learning classifiers from imbalanced data.
Although several methods for improving classifiers have been introduced, the identification …

Canonical machine learning algorithms assume that the number of objects in the considered
classes are roughly similar. However, in many real-life situations the distribution of examples …

Real-world data is never perfect and can often suffer from corruptions (noise) that may
impact interpretations of the data, models created from the data and decisions made based …

Diagnosis, being the first step in medical practice, is very crucial for clinical decision making.
This paper investigates state-of-the-art computational intelligence (CI) techniques applied in …

Currently, the best way to reduce the mortality of cancer is to detect and treat it in the earliest
stages. Technological advances in genomics and proteomics have opened a new realm of …

This paper compares the performance of several boosting and bagging techniques in the
context of learning from imbalanced and noisy binary-class data. Noise and class imbalance …