With the rapid development of information technology, Big data has become a hot topic of
research in governments, academia, and enterprises. On the one hand, Big data brings …

Significant challenges arise when Graph Neural Networks (GNNs) try to deal with uneven
data. Specifically in signed and weighted graph structures. This makes classification tasks …

In real-world machine learning applications, unlabeled training data are readily available,
but labeled data are expensive and hard to obtain. Therefore, semi-supervised learning …

Circuit learning has gained significant attention due to machine learning advancements and
approximate synthesis applications. The task is to learn a circuit to model an unknown …

Traditional supervised classification models aim to approximate the functional mapping
between instance attributes and their class labels. These models, however, do not consider …

Trusting an algorithmic decision is much easier if it is understood how it was achieved.
Therefore, data mining algorithms with explainable abilities are preferred over complex …

Understanding the reason why a prediction has been made by a machine is crucial to grant
trust to a human decision-maker. However, data mining based decision support systems are …

In this paper, a new multi-class classification technology combining fuzzy neural network
and ensemble learning technology is proposed. Several fuzzy neural networks, including …

Data stream mining has attracted much attention of the machine learning community in the
last decade. Motivated by the upcoming issues associated with data stream applications …

The accuracy performance of a classification system strongly depends on the quality of the
data used to train it. Among other issues, noise in the attribute values degrades data quality …