Concept drift techniques aim at learning patterns from data streams that may change over
time. Although such behavior is not usually expected in controlled environments, real-world …

This paper presents a comprehensive review of evolutionary algorithms that learn an
ensemble of predictive models for supervised machine learning (classification and …

One popular approach employed to tackle classification problems in a static environment
consists in using a Dynamic Classifier Selection (DCS)-based method to select a custom …

In dynamic ensemble selection (DES) techniques, the competence level of each classifier is
estimated from a pool of classifiers, and only the most competent ones are selected to …

A common problem in machine learning is to find representative real-world labeled datasets
to put the methods to test. When develo** approaches to deal with concept drifts, some …

Fraud detection has been a difficult problem in the industry for the past many years which
has caused a massive financial loss for individuals/organizations. Machine learning …

This work presents a new model for the automatic synthesis of fuzzy classifiers, based on
quantum-inspired evolutionary algorithms, which overcomes the difficulties inherent to the …

Classification is the machine learning task of categorizing instances into classes. There are
several algorithms in the literature that perform classification, with varying degrees of …

Classification algorithms take their decisions according to a learning process on the training
set. Therefore, the data to be classified in the test set must have the same distribution as the …