Browsing by Author "van Rijn, Jan N."
Now showing items 1-5 of 5
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Algorithm selection on data streams
(Springer International Publishing, 2014)We explore the possibilities of meta-learning on data streams, in particular algorithm selection. In a first experiment we calculate the characteristics of a small sample of a data stream, and try to predict which classifier ... -
Case study on bagging stable classifiers for data streams
(2015)Ensembles of classifiers are among the strongest classi-fiers in most data mining applications. Bagging ensembles exploit the instability of base-classifiers by training them on different bootstrap replicates. It has been ... -
Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams
(IEEE, 2015-01-01)Ensembles of classifiers are among the best performing classifiers available in many data mining applications. However, most ensembles developed specifically for the dynamic data stream setting rely on only one type of ... -
The online performance estimation framework: heterogeneous ensemble learning for data streams
(Springer, 2018)Ensembles of classifiers are among the best performing classifiers available in many data mining applications, including the mining of data streams. Rather than training one classifier, multiple classifiers are trained, ... -
Towards Meta-learning over Data Streams
(CEUR-WS, 2014)Modern society produces vast streams of data. Many stream mining algorithms have been developed to capture general trends in these streams, and make predictions for future observations, but relatively little is known about ...
