Machine learning methods have achieved good performance and been widely applied in
various real-world applications. They can learn the model adaptively and be better fit for …

Feature subset selection is an important problem in knowledge discovery, not only for the
insight gained from determining relevant modeling variables, but also for the improved …

Neural network ensembles are gaining popularity by harnessing the complementary wisdom
of multiple base models. Ensemble teams with high diversity promote high failure …

As witnessed by a vast corpus of literature, dimensionality reduction is a fundamental step
for biomedical data analysis. Indeed, in this domain, there is often the need for coping with a …

Understanding the performance of machine learning models across diverse data
distributions is critically important for reliable applications. Motivated by this, there is a …

Contrastive learning methods have significantly narrowed the gap between supervised and
unsupervised learning on computer vision tasks. In this paper, we explore their application …

Feature selection is often applied to high-dimensional data prior to classification learning.
Using the same training dataset in both selection and learning can result in so-called feature …

In many important machine learning applications, the training distribution used to learn a
probabilistic classifier differs from the distribution on which the classifier will be used to make …

Gradient-based learning algorithms have an implicit simplicity bias which in effect can limit
the diversity of predictors being sampled by the learning procedure. This behavior can …

Ensembles, as a widely used and effective technique in the machine learning community,
succeed within a key element—“diversity.” The relationship between diversity and …