Training set size reduction in large dataset problems
© 2015 IEEE. Classifiers have known to be used in various fields of applications. However, the main problem usually found recently is about applying a classifier to large datasets. Thus, the process of reducing size of the training set becomes necessary especially to accelerate the processing time o...
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| Main Authors: | , , |
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| 格式: | Conference Proceeding |
| 出版: |
2017
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| 在線閱讀: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84964320834&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/42095 |
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| 機構: | Chiang Mai University |
| 總結: | © 2015 IEEE. Classifiers have known to be used in various fields of applications. However, the main problem usually found recently is about applying a classifier to large datasets. Thus, the process of reducing size of the training set becomes necessary especially to accelerate the processing time of the classifier. Concerning the problem, this paper proposes a new method which can reduce size of the training set in a large dataset. Our proposed method is improved from a famous graph-based algorithm named Optimum-Path Forest (OPF). Our principal concept of reducing the training set's size is to utilize the Segmented Least Square Algorithm (SLSA) in estimating the tree's shape. From the experimental results, our proposed method could reduce size of the training set by about 7 to 21 percent comparing with the original OPF algorithm while the classification's accuracy decreased insignificantly by only about 0.2 to 0.5 percent. In addition, for some datasets, our method provided even as same degree of accuracy as of the original OPF algorithm. |
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