Name
Title | ||
|---|---|---|
A Theoretical Framework on the Ideal Number of Classifiers for Online Ensembles in Data Streams |
Abstract | ||
|---|---|---|
A priori determining the ideal number of component classifiers of an ensemble is an important problem. The volume and velocity of big data streams make this even more crucial in terms of prediction accuracies and resource requirements. There are a limited number of studies addressing this problem for batch mode and none for online environments. Our theoretical framework shows that using the same number of independent component classifiers as class labels gives the highest accuracy. We prove the existence of an ideal number of classifiers for an ensemble, using the weighted majority voting aggregation rule. In our experiments, we use two state-of-the-art online ensemble classifiers with six synthetic and six real-world data streams. The violation of providing independent component classifiers for our theoretical framework makes determining the exact ideal number of classifiers nearly impossible. We suggest upper bounds for the number of classifiers that gives the highest accuracy. An important implication of our study is that comparing online ensemble classifiers should be done based on these ideal values, since comparing based on a fixed number of classifiers can be misleading. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2983323.2983907 | ACM International Conference on Information and Knowledge Management |
Keywords | Field | DocType |
Ensemble size,voting framework,big data stream | Data mining,Ideal number,Data stream mining,Computer science,Random subspace method,A priori and a posteriori,Cascading classifiers,Artificial intelligence,Batch processing,Majority rule,Big data,Machine learning | Conference |
Citations | PageRank | References |
4 | 0.41 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hamed Rezanejad Asl-Bonab | 1 | 14 | 3.05 |
| Fazli Can | 2 | 581 | 94.63 |