Random Projection in the Presence of Concept Drift in Supervised Environments

Heusinger, Moritz; Schleif, Frank-Michael

doi:10.1007/978-3-030-61401-0_48

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

Publication Types

Select...

Other1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar to our work, Pham et al [15] and Heusinger et al [10] reported that they improved the accuracy of ensemble prediction or reduced the execution time by giving different mappings to different base learners in an online environment. Both of these papers discuss implementing random projection [11] techniques in an online environment.…”

Section: Introductionsupporting

confidence: 88%

Adaptive Rotation Forests: Decision Tree Ensembles for Sequential Learning

Sugawara

Oyama

Kurihara

2021

2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

View full text Add to dashboard Cite

We have developed an ensemble-based approach for online machine learning: adaptive rotation forest and AD-WIN adaptive rotation forest. We focused on rotation forest, an offline supervised ensemble algorithm with a particularly high prediction accuracy while all the features are continuous. Our objective was to develop a high-performance online ensemble method that uses a process similar to that of rotation forest in an online environment. Our experiments demonstrated that the proposed approach simplifies the tree structure used for the base learners, reduces memory consumption, and improves prediction accuracy for some data streams.

show abstract

Section: Introductionsupporting

confidence: 88%