Active Learning With Drifting Streaming Data

By Indrė Žliobaitė, Albert Bifet, Bernhard Pfahringer, G. Holmes. IEEE Transactions on Neural Networks and Learning Systems, 2014

368 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Learning from imbalanced data: open challenges and future directions (2016, background): “, by active learning) is currently of crucial importance in this field [69].”
Learning in Nonstationary Environments: A Survey (2015, methodology): “In unsupervised learning/clustering, the learning is done without using labeled data [114]–[116], whereas in active learning (not to be confused with active approaches to learning in nonstationary environments, discussed in Section III-A) the algorithm identifies the most important instances for the”
Learning under Concept Drift: A Review (2019, background): “2 Covertype [108] 581012 54 7 [13], [23], [35], [36], [59], [64], [74], [83], [84], [85], [88], [89], [104], [117]”
A survey on data preprocessing for data stream mining: Current status and future directions (2017, background): “Active learning strategies allow to select only the most relevant samples for labeling [52, 53].”
Online and Non-Parametric Drift Detection Methods Based on Hoeffding’s Bounds (2015, background): “Although in this paper we study the concept drift problem assuming that all the incoming instances are labeled, the proposed change detectors can also be applied to more realistic scenarios in which instances arrive online but labels are harder to obtain [11].”