• Publications
  • Influence
MOA: Massive Online Analysis
TLDR
MOA includes a collection of offline and online methods as well as tools for evaluation that implements boosting, bagging, and Hoeffding Trees, all with and without Naive Bayes classifiers at the leaves. 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
Learning from Time-Changing Data with Adaptive Windowing
TLDR
A new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time is presented, using sliding windows whose size is recomputed online according to the rate of change observed from the data in the window itself. Expand
Early Drift Detection Method
TLDR
A method for detecting concept drift, even in the case of slow gradual change, based on the estimated distribution of the distances between classiflcation errors that can be used with any learning algorithm in two ways: using it as a wrapper of a batch learning algorithm or implementing it inside an incremental and online algorithm. Expand
New ensemble methods for evolving data streams
TLDR
A new experimental data stream framework for studying concept drift, and two new variants of Bagging: ADWIN Bagging and Adaptive-Size Hoeffding Tree (ASHT) Bagging are proposed. Expand
Active Learning With Drifting Streaming Data
TLDR
This paper presents a theoretically supported framework for active learning from drifting data streams and develops three active learning strategies for streaming data that explicitly handle concept drift, based on uncertainty, dynamic allocation of labeling efforts over time, and randomization of the search space. Expand
Sentiment Knowledge Discovery in Twitter Streaming Data
TLDR
To deal with streaming unbalanced classes, a sliding window Kappa statistic is proposed for evaluation in time-changing data streams, and a study on Twitter data is performed using learning algorithms for data streams. Expand
Mining big data: current status, and forecast to the future
TLDR
This issue introduces four articles, written by influential scientists in the field, covering the most interesting and state-of-the-art topics on Big Data mining, and presents a broad overview of the topic, its current status, controversy, and a forecast to the future. Expand
Leveraging Bagging for Evolving Data Streams
TLDR
A new variant of bagging is proposed, called leveraging bagging, which combines the simplicity of baging with adding more randomization to the input, and output of the classifiers. Expand
Adaptive Learning from Evolving Data Streams
TLDR
A method for developing algorithms that can adaptively learn from data streams that drift over time, based on using change detectors and estimator modules at the right places and choosing implementations with theoretical guarantees in order to extend such guarantees to the resulting adaptive learning algorithm. Expand
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