| Autor: |
Yan Shen, Jianguo Du, Jingen Tong, Qian Dou, Luyi Jing |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 8, Pp 64157-64168 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2020.2984154 |
| Popis: |
There are lots of typical applications of classification learning for accumulated big data in the nonstationary environments. It is very necessary and urgent to study the algorithms that can carry out classification learning efficiently in these environments. The recently proposed algorithm, named Learn++.NSE, has made an important breakthrough, which is one of the important research achievements in this research field. However, the Learn++.NSE algorithm adopts a serial ensemble mechanism, and its execution efficiency needs to be further improved when facing the long-term accumulated big data. A Parallel and Reverse Learn++.NSE algorithm, abbreviated as PRLearn++.NSE, is proposed in this paper by changing the ensemble mechanism of the base-classifiers, which uses the old base-classifiers as a supplement to the new base-classifier. It constructs a fast and parallel ensemble mechanism. The experimental results on the artificially generated dataset and real dataset show that the proposed PRLearn++.NSE algorithm can greatly improve the efficiency of ensemble classification learning under the premise of obtaining the approaching classification accuracy of Learn++.NSE algorithm and it is very suitable for fast ensemble classification learning for the long-term accumulated big data. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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