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Learning with Drift Detection
TLDR
A method for detection of changes in the probability distribution of examples, to control the online error-rate of the algorithm and to observe that the method is independent of the learning algorithm. Expand
A survey on concept drift adaptation
TLDR
The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art and aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners. Expand
Knowledge discovery from data streams
TLDR
5 papers from the accepted papers of the Fourth International Workshop on Knowledge Discovery from Data Streams that goes from recommendation algorithms, Clustering, Drifting Concepts and Frequent pattern mining are selected, the common concept in all the papers is that learning occurs while data continuously flows. Expand
Predicting Taxi–Passenger Demand Using Streaming Data
TLDR
A novel methodology for predicting the spatial distribution of taxi-passengers for a short-term time horizon using streaming data and demonstrates that the proposed framework can provide effective insight into the spatiotemporal distribution of Taxi-passenger demand for a 30-min horizon. Expand
Learning model trees from evolving data streams
TLDR
An efficient and incremental stream mining algorithm which is able to learn regression and model trees from possibly unbounded, high-speed and time-changing data streams and which improves the any-time performance and greatly reduces the costs of adaptation. Expand
Accurate decision trees for mining high-speed data streams
TLDR
The VFDT system is extended in two directions: the ability to deal with continuous data and the use of more powerful classification techniques at tree leaves, which can incorporate and classify new information online, with a single scan of the data, in time constant per example. Expand
Cascade Generalization
TLDR
Two related methods for merging classifiers are presented, one of which outperforms other methods for combining classifiers, like Stacked Generalization, and competes well against Boosting at statistically significant confidence levels. Expand
Ensemble learning for data stream analysis: A survey
TLDR
This paper surveys research on ensembles for data stream classification as well as regression tasks and discusses advanced learning concepts such as imbalanced data streams, novelty detection, active and semi-supervised learning, complex data representations and structured outputs. Expand
Data stream clustering: A survey
TLDR
A survey of data stream clustering algorithms is presented, providing a thorough discussion of the main design components of state-of-the-art algorithms and an overview of the usually employed experimental methodologies. Expand
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