[1]ZHANG Bencai,WANG Zhihai,SUN Yange.An ensemble classification algorithm based on diversity and accuracy weighting for data streams[J].CAAI Transactions on Intelligent Systems,2019,14(1):179-185.[doi:10.11992/tis.201806021]
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An ensemble classification algorithm based on diversity and accuracy weighting for data streams
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
14
Number of periods:
2019 1
Page number:
179-185
Column:
学术论文—人工智能基础
Public date:
2019-01-05
- Title:
- An ensemble classification algorithm based on diversity and accuracy weighting for data streams
- Keywords:
- data stream; concept drift; diversity; accuracy; ensemble learning; data chunk; value measurement; MOA
- CLC:
- TP391
- DOI:
- 10.11992/tis.201806021
- Abstract:
- To overcome the effect of concept drift on data stream classification, we propose an ensemble classification algorithm based on diversity and accuracy weighting named DAWE. The difference between DAWE and other existing ensemble methods is that DAWE considers both diversity and accuracy. The classifier’s accuracy on the new data chunk and its diversity in the ensemble were linearly weighted to measure the value of the current ensemble classifier and the measured value was applied to the substitute strategy of the base classifier. The DAWE algorithm proposed in this paper was experimentally compared with the latest algorithms in massive online analysis (MOA), using both synthetic and real-world datasets. Experiments showed that the method proposed in this paper was effective and the average overall accuracy of the data sets was superior to that of other algorithms. Overall, this method can effectively manage concept drift in data stream mining.
- References:
-
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Last Update:
1900-01-01