Class imbalance poses new challenges when it comes to classifying data streams. Many
algorithms recently proposed in the literature tackle this problem using a variety of data …

In IoT environment applications generate continuous non-stationary data streams with in-
built problems of concept drift and class imbalance which cause classifier performance …

This paper concerns a new incremental approach to mining data streams. It is known that
patterns in a data stream may evolve over time. In many cases, we need to track and …

Abstract Concept drifts and class imbalance are two primary challenges in supervised data
stream classification, whereas their co-occurrence presents a more complicated learning …

With the rapid development of data stream, multi-label algorithms for mining dynamic data
become more and more important. At the same time, when data distribution changes …

The imbalance and concept drift problems in data streams become more complex in multi-
class environment, and extreme imbalance and variation in class ratio may also exist. To …

This paper proposes a novel algorithm for incremental learning over streaming data in a non-
stationary environment. The idea refers to the applicability of Probabilistic Neural Networks …

The prevalence of streams in practical applications is rapidly increasing, making stream data
mining increasingly important. However, unlike the static datasets used in machine learning …

The increasing integration of machine learning (ML) in modern software systems has lead to
new challenges as a result of the shift from human-determined behavior to data-determined …

This paper presents an approximative online algorithm to perform the Kolmogorov–Smirnov
test. There is a ubiquitous need for evaluating the fitness between statistical distributions …