Titlebook: Learning from Data Streams in Evolving Environments; Methods and Applicat Moamar Sayed-Mouchaweh Book 2019 Springer International Publishin

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Large-Scale Learning from Data Streams with Apache SAMOA,nd regression, as well as programming abstractions to develop new algorithms. It features a pluggable architecture that allows it to run on several distributed stream processing engines such as Apache Flink, Apache Storm, and Apache Samza. Apache SAMOA is written in Java and is available at . under the Apache Software License version 2.0.

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Analyzing and Clustering Pareto-Optimal Objects in Data Streams,uires new algorithms and methods to be able to learn under the evolving and unbounded data. In this chapter we focus on the task of .. We show that this method is a real alternative to the state-of-the-art approaches.

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Transfer Learning in Non-stationary Environments,that come from different probability distributions. However, these two fields have evolved separately. Transfer learning enables knowledge to be transferred between different domains or tasks in order to improve predictive performance in a target domain and task. It has no notion of continuing time.

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A New Combination of Diversity Techniques in Ensemble Classifiers for Handling Complex Concept Driflly in dynamic environments, data are presented as streams that may evolve over time and this is known by .. Handling concept drift through ensemble classifiers has received a great interest in last decades. The success of these ensemble methods relies on their .. Accordingly, various diversity tech

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Analyzing and Clustering Pareto-Optimal Objects in Data Streams,der to learn from this ever-growing amount of data. Although many approaches exist for effective processing of data streams, learning from streams requires new algorithms and methods to be able to learn under the evolving and unbounded data. In this chapter we focus on the task of .. We show that th

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Error-Bounded Approximation of Data Stream: Methods and Theories,ention recently. To efficiently process and explore data streams, the compact data representation is playing an important role, since the data approximations other than the original data items are usually applied in many stream mining tasks, such as clustering, classification, and correlation analys

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Ensemble Dynamics in Non-stationary Data Stream Classification,ost cases, can be read only once by the data mining algorithm. One of the most challenging problems in this process is how to learn such models in non-stationary environments, where the data/class distribution evolves over time. This phenomenon is called .. Ensemble learning techniques have been pro