The primary focus of machine learning has traditionally been on learning from data assumed
to be sufficient and representative of the underlying fixed, yet unknown, distribution. Such …

Anomaly detection in a WSN is an important aspect of data analysis in order to identify data
items that significantly differ from normal data. A characteristic of the data generated by a …

We introduce an ensemble of classifiers-based approach for incremental learning of concept
drift, characterized by nonstationary environments (NSEs), where the underlying data …

The process of continuously reallocating funds into financial assets, aiming to increase the
expected return of investment and minimizing the risk, is known as portfolio management. In …

Learning in nonstationary environments, also known as learning concept drift, is concerned
with learning from data whose statistical characteristics change over time. Concept drift is …

Multi-agent reinforcement learning (MARL) is a widely researched technique for
decentralised control in complex large-scale autonomous systems. Such systems often …

In this paper we propose and evaluate an evolutionary-based hyper-heuristic approach,
called EH-DVRP, for solving hard instances of the dynamic vehicle routing problem. A hyper …

The data imbalance problem hampers the classification task. In streaming environments, this
becomes even more cumbersome as the proportion of classes can vary over time …

Learning in a non-stationary environment and in the presence of class imbalance has been
receiving more recognition from the computational intelligence community, but little work has …

Learning efficient predictive models in dynamic environments requires taking into account
the continuous changing nature of phenomena generating the data streams, known in …