Background and objectives Diabetic retinopathy (DR) is the leading cause of blindness
worldwide, and therefore its early detection is important in order to reduce disease-related …

Several AutoML tools aim to facilitate the usability of machine learning algorithms,
automatically recommending algorithms using techniques such as meta-learning, grid …

In cancer genomics, variant calling has advanced, but traditional mean accuracy evaluations
are inadequate for biomarkers like tumor mutation burden, which vary significantly across …

The paper describes the study on the problem of applying classification techniques in
medical datasets with a class imbalance. The aim of the research is to identify factors that …

Classification is a data mining technique that is used to predict the future by using available
data and aims to discover hidden relations between variables and classes. Since the target …

Training Deep Neural Networks (DNNs) with imbalanced data is indeed a daunting task.
Categories with limited training data often suffer from poor representation, resulting in …

Machine learning applications are challenged by data difficulty factors, which are
responsible for the degradation of data quality and dealing with them is a demanding task …

Este trabalho apresenta uma solução de recomendação de algoritmos de detecção de ruído
por meio de técnicas de Meta-Aprendizado (MtL). Primeiramente, foi realizada uma revisão …

Imbalanced datasets are pervasive and comprise many real-world applications, such as
medical diagnosis and software fault detection. As common classifiers assume a balanced …

Training Deep Neural Networks (DNN) with imbalanced data is a challenging task. The
categories with small amounts of training data often encounter poor representation, which …