… Several theoretical and empirical studies have demonstrated … to better accuracy than a
single classifier (Bifet & Gavaldà, … automatic algorithm configuration for data stream mining can …

… well as empirical analysis of big data streams and technologies are still open for further research
… that although, significant research efforts have been directed to real-time analysis of big …

… 3) Empirical Analysis: We compare SRP against stateof-the-art ensemble variants for …
classification models from evolving data streams. In Section III, we present the SRP algorithm and …

… In this paper, we will focus on data streams classification. Although there is a plethora of …
overview of data stream mining, concept drift, ensemble classifiers for data streams, and …

… However, in the online scenario of data stream mining, we still face some primary challenges
and difficulties related to the comparison and evaluation of new proposals, mainly due to …

… of stream clustering. Most importantly, we describe how data streams are typically aggregated
and how algorithms … a rigorous empirical comparison of the most popular stream clustering …

… streams, time window models and outlier detection. We comprehensively review recent data
stream clustering algorithms and analyze them in … tools that are used for data stream mining. …

… data generators used by researchers devoted to machine learning using empirical analysis
of machine learning algorithms [… the maximum dimensionality of data in a multivariate dataset …

… A software framework that implements many of the techniques … is to present the techniques
in data stream mining to three … Many data stream mining techniques in this book are …

… a literature survey of classification algorithm recommendation methods. The … classifier
selection through meta-learning and comprehensively discusses the different phases of classifier …