Feature selection is one of the key problems for machine learning and data mining. In this
review paper, a brief historical background of the field is given, followed by a selection of …

In this work, we present a review of the state of the art of learning vector quantization (LVQ)
classifiers. A taxonomy is proposed which integrates the most relevant LVQ approaches to …

We propose a new matrix learning scheme to extend relevance learning vector quantization
(RLVQ), an efficient prototype-based classification algorithm, toward a general adaptive …

The proportion of instances belonging to each class in a data-set plays an important role in
machine learning. However, the real world data often suffer from class imbalance. Dealing …

Feature selection is the task of choosing a small set out of a given set of features that capture
the relevant properties of the data. In the context of supervised classification problems the …

RELIEF is considered one of the most successful algorithms for assessing the quality of
features. In this paper, we propose a set of new feature weighting algorithms that perform …

With the rapid development of digital imaging and communication technologies, image set-
based face recognition (ISFR) is becoming increasingly important. One key issue of ISFR is …

Diversity within base classifiers has been recognized as an important characteristic of an
ensemble classifier. Data and feature sampling are two popular methods of increasing such …

Federated Learning (FL) is a framework which enables distributed model training using a
large corpus of decentralized training data. Existing methods aggregate models …

Feature selection from gene expression microarray data is a widely used technique for
selecting candidate genes in various cancer studies. Besides predictive ability of the …