References

doi:10.1002/9781118914564.refs

Search citation statements

Order By: Relevance

Paper Sections

Select...

Incremental Learning and Ensemble Methods1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2016

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 283 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2) creates a set of base classifiers from training data and classify new instances by poling of base ensembles [11]. Ensembles are popular because they improve classification accuracy in static environments [12]. But they need some changes to adapt with dynamic nature of data streams.…”

Section: Incremental Learning and Ensemble Methodsmentioning

confidence: 99%

A review on data stream classification approaches

Homayoun

Ahmadzadeh

2016

JACST

View full text Add to dashboard Cite

Stream data is usually in vast volume, changing dynamically, possibly infinite, and containing multi-dimensional features. The attention towards data stream mining is increasing as regards to its presence in wide range of real-world applications, such as e-commerce, banking, sensor data and telecommunication records. Similar to data mining, data stream mining includes classification, clustering, frequent pattern mining etc. techniques; the special focus of this paper is on classification methods invented to handle data streams. Early methods of data stream classification needed all instances to be labeled for creating classifier models, but there are some methods (SemiSupervised Learning and Active Learning) in which unlabeled data is employed as well as labeled data. In this paper, by focusing on ensemble methods, semi-supervised and active learning, a review on some state of the art researches is given.

show abstract

Section: Incremental Learning and Ensemble Methodsmentioning

confidence: 99%