Paper Info
Title
Accurate Ensembles for Data Streams: Combining Restricted Hoeffding Trees using Stacking
Abstract
The success of simple methods for classification shows that is is often not necessary to model complex attribute interactions to obtain good classification accuracy on practical problems. In this paper, we propose to exploit this phenomenon in the data stream context by building an ensemble of Hoeffding trees that are each limited to a small subset of attributes. In this way, each tree is restricted to model interactions between attributes in its corresponding subset. Because it is not known a priori which attribute subsets are relevant for prediction, we build exhaustive ensembles that consider all possible attribute subsets of a given size. As the resulting Hoeffding trees are not all equally important, we weigh them in a suitable manner to obtain accurate classifications. This is done by combining the log-odds of their probability estimates using sigmoid perceptrons,with one perceptron per class. We propose a mechanism for setting the perceptrons' learning rate using the ADWIN change detection method for data streams, and also use ADWIN to reset ensemble members (i.e. Hoeffding trees) when they no longer perform well. Our experiments show that the resulting ensemble classifier outperforms bagging for data streams in terms of accuracy when both are used in conjunction with adaptive naive Bayes Hoeffding trees, at the expense of runtime and memory consumption.
Year
Venue
Keywords
2010
JMLR Workshop and Conference Proceedings
Data Streams,Decision Trees,Ensemble Methods
DocType
Volume
ISSN
Journal
13
1938-7288
Citations 
PageRank 
References 
19
0.97
0
Authors
6
Name
Order
Citations
PageRank
Albert Bifet12659140.83
Eibe Frank211555619.59
Geoffrey Holmes310365473.46
Bernhard Pfahringer410252494.74
Masashi Sugiyama53353264.24
Qiang Yang617039875.69