Paper Info
Title
Vertical and Horizontal Partitioning in Data Stream Regression Ensembles
Abstract
Data stream mining is an emerging topic in machine learning that targets the creation and update of predictive models over time as new data becomes available. Regarding existing works, classification is the most widely tackled task, which leaves regression nearly untouched. In this paper, the focus relies on ensemble learning for data stream regression, more specifically on vertical and horizontal data partitioning techniques. The goal is to determine whether and under which conditions partitioning can lessen the error rates of different types of learners in the data stream regression task. The proposed method combines vertical and horizontal partitioning, and it is compared with and against different types of learners and existing ensembles.
Year
DOI
Venue
2019
10.1109/IJCNN.2019.8852244
2019 International Joint Conference on Neural Networks (IJCNN)
Keywords
Field
DocType
data stream mining,regression,bagging,random subspaces
Horizontal and vertical,Data stream mining,Regression,Computer science,Data stream,Artificial intelligence,Ensemble learning,Data partitioning,Machine learning
Conference
ISSN
ISBN
Citations 
2161-4393
978-1-7281-1986-1
0
PageRank 
References 
Authors
0.34
13
1
Name
Order
Citations
PageRank
Jean Paul Barddal114016.77