Accuracy weighted diversity-based online boosting
| Autor: | Nagaraj Honnikoll, Ishwar Baidari |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Data stream
0209 industrial biotechnology Boosting (machine learning) Concept drift Computer science business.industry General Engineering 02 engineering and technology Machine learning computer.software_genre Computer Science Applications Weighting 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | Expert Systems with Applications. 160:113723 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2020.113723 |
| Popis: | Target distributional change occurring in a data stream known as concept drift, causes a challenging task for an online learning method, as the accuracy of an online learning method may decrease due to these changes. In this paper, the Accuracy Weighted Diversity-based Online Boosting (AWDOB) method has been proposed, which is based on Adaptable Diversity-based Online Boosting (ADOB) and, other modifications. More precisely, AWDOB uses the proposed accuracy weighting scheme which is based on previous expert’s results of the sums of correctly classified and incorrectly classified instances to calculate the weight of current expert, which improved the overall accuracy of the AWDOB. Experiments were conducted to compare the accuracy results of AWDOB against other methods using ten real-world datasets and thirty-two artificial datasets. Artificial datasets were generated by the four artificial data generators which included gradual and abrupt concept drifts within them. Experimental results suggest that AWDOB beats the accuracy results of other tested methods. |
| Databáze: | OpenAIRE |
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