Name
Title | ||
|---|---|---|
Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm |
Abstract | ||
|---|---|---|
Data stream mining has been receiving increased attention due to its presence in a wide range of applications, such as sensor networks, banking, and telecommunication. One of the most important challenges in learning from data streams is reacting to concept drift, i.e., unforeseen changes of the stream's underlying data distribution. Several classification algorithms that cope with concept drift have been put forward, however, most of them specialize in one type of change. In this paper, we propose a new data stream classifier, called the Accuracy Updated Ensemble (AUE2), which aims at reacting equally well to different types of drift. AUE2 combines accuracy-based weighting mechanisms known from block-based ensembles with the incremental nature of Hoeffding Trees. The proposed algorithm is experimentally compared with 11 state-of-the-art stream methods, including single classifiers, block-based and online ensembles, and hybrid approaches in different drift scenarios. Out of all the compared algorithms, AUE2 provided best average classification accuracy while proving to be less memory consuming than other ensemble approaches. Experimental results show that AUE2 can be considered suitable for scenarios, involving many types of drift as well as static environments. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TNNLS.2013.2251352 | Neural Networks and Learning Systems, IEEE Transactions |
Keywords | Field | DocType |
data mining,learning (artificial intelligence),pattern classification,trees (mathematics),AUE2,Hoeffding trees,accuracy updated ensemble algorithm,accuracy-based weighting mechanisms,block-based ensembles,classification algorithms,concept drift,data stream classifier,data stream learning,data stream mining,online ensembles,single classifiers,Concept drift,data stream mining,ensemble classifier,nonstationary environments | Data mining,Data stream mining,Weighting,Data stream,Computer science,Artificial intelligence,Classifier (linguistics),Pattern recognition,Algorithm,Concept drift,Statistical classification,Wireless sensor network,Machine learning | Journal |
Volume | Issue | ISSN |
25 | 1 | 2162-237X |
Citations | PageRank | References |
95 | 2.03 | 24 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dariusz Brzezinski | 1 | 213 | 11.28 |
| Jerzy Stefanowski | 2 | 1653 | 139.25 |