Real-life datasets are often imbalanced, that is, there are significantly more training samples
available for some classes than for others, and consequently the conventional aim of …

Regression conformal prediction produces prediction intervals that are valid, ie, the
probability of excluding the correct target value is bounded by a predefined confidence level …

In this paper, we present boosted SVM dedicated to solve imbalanced data problems.
Proposed solution combines the benefits of using ensemble classifiers for uneven data …

Rare events are involved in many challenging real world classification problems, where the
minority class is usually the most expensive to sample and to label. As a consequence …

In the field of Data Mining, the estimation of the quality of the learned models is a key step in
order to select the most appropriate tool for the problem to be solved. Traditionally, a k-fold …

There are many real-world classification problems involving multiple classes, eg, in
bioinformatics, computer vision, or medicine. These problems are generally more difficult …

Subgroup discovery (SD) is a descriptive data mining technique using supervised learning.
In this article, we review the use of evolutionary algorithms (EAs) for SD. In particular, we will …

The presence of noise in data is a common problem that produces several negative
consequences in classification problems. In multi-class problems, these consequences are …

The classical fuzzy system modeling methods implicitly assume data generated from a
single task, which is essentially not in accordance with many practical scenarios where data …

In recent years, many nearest neighbor algorithms based on fuzzy sets theory have been
developed. These methods form a field, known as fuzzy nearest neighbor classification …