On the Window Size for Classification in Changing Environments

Classification in changing environments (commonly known as concept drift) requires adaptation of the classifier to accommodate the changes. One approach is to keep a moving window on the streaming data and constantly update the classifier on it. Here we consider an abrupt change scenario where one set of probability distributions of the classes is instantly replaced with another. For a fixed ‘transition period’ around the change, we derive a generic relationship between the size of the moving window and the classification error rate. We derive expressions for the error in the transition period and for the optimal window size for the case of two Gaussian classes where the concept change is a geometrical displacement of the whole class configuration in the space. A simple window resize strategy based on the derived relationship is proposed and compared with fixed-size windows on a real benchmark data set data set (Electricity Market)

Adaptive machine learning for automated modeling of residential prosumer agents

An efficient participation of prosumers in power system management depends on the quality of information they can obtain. Prosumers actions can be performed by automated agents that are operating in time-changing environments. Therefore, it is essential for them to deal with data stream problems in order to make reliable decisions based on the most accurate information. This paper provides an in-depth investigation of data and concept drift issues in accordance with residential prosumer agents. Additionally, the adaptation techniques, forgetting mechanisms, and learning strategies employed to handle these issues are explored. Accordingly, an approach is proposed to adapt the prosumer agent models to overcome the gradual and sudden concept drift concurrently. The suggested method is based on triggered adaptation techniques and performance-based forgetting mechanism. The results obtained in this study demonstrate that the proposed approach is capable of constructing efficient prosumer agents models with regard to the concept drift proble

On utilizing weak estimators to achieve the online classification of data streams

Author's accepted version (post-print).Available from 03/09/2021.acceptedVersio

Intelligent Learning Automata-based Strategies Applied to Personalized Service Provisioning in Pervasive Environments

Doktorgradsavhandling i informasjons- og kommunikasjonsteknologi, Universitetet i Agder, Grimstad, 201

Tracking Drifting Concepts by Time Window Optimisation – Research and Development in Intelligent Systems

This paper addresses the task of learning concept descriptions from streams of data. As new data are obtained the concept description has to be updated regularly to include the new data. In this case we can face the problem that the concept changes over time. Hence the old data become irrelevant to the current concept and have to be removed from the training dataset. This problem is known in the area of machine learning as concept drift. We develop a mechanism that tracks changing concepts using an adaptive time window. The method uses a significance test to detect concept drift and then optimizes the size of the time window, aiming to maximise the classification accuracy on recent data. The method presented is general in nature and can be used with any learning algorithm. The method is tested with three standard learning algorithms (kNN, ID3 and NBC). Three datasets have been used in these experiments. The experimental results provide evidence that the suggested forgetting mechanism is able significantly to improve predictive accuracy on changing concepts