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[ subject:"Industrial engineering." ]
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Hybrid classification method for imb...
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Gao, Tianxiang.
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Hybrid classification method for imbalanced dataset.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Hybrid classification method for imbalanced dataset./
作者:
Gao, Tianxiang.
面頁冊數:
44 p.
附註:
Source: Masters Abstracts International, Volume: 54-06.
Contained By:
Masters Abstracts International54-06(E).
標題:
Industrial engineering. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1593015
ISBN:
9781321890808
Hybrid classification method for imbalanced dataset.
Gao, Tianxiang.
Hybrid classification method for imbalanced dataset.
- 44 p.
Source: Masters Abstracts International, Volume: 54-06.
Thesis (M.S.)--Iowa State University, 2015.
The research area of imbalanced dataset has been attracted increasing attention from both academic and industrial areas, because it poses a serious issues for so many supervised learning problems. Since the number of majority class dominates the number of minority class are from minority class, if training dataset includes all data in order to fit a classic classifier, the classifier tends to classify all data to majority class by ignoring minority data as noise. Thus, it is very significant to select appropriate training dataset in the prepossessing stage for classification of imbalanced dataset. We propose an combination approach of SMOTE (Synthetic Minority Over-sampling Technique) and instance selection approaches. The numeric results show that the proposed combination approach can help classifiers to achieve better performance.
ISBN: 9781321890808Subjects--Topical Terms:
526216
Industrial engineering.
Hybrid classification method for imbalanced dataset.
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The research area of imbalanced dataset has been attracted increasing attention from both academic and industrial areas, because it poses a serious issues for so many supervised learning problems. Since the number of majority class dominates the number of minority class are from minority class, if training dataset includes all data in order to fit a classic classifier, the classifier tends to classify all data to majority class by ignoring minority data as noise. Thus, it is very significant to select appropriate training dataset in the prepossessing stage for classification of imbalanced dataset. We propose an combination approach of SMOTE (Synthetic Minority Over-sampling Technique) and instance selection approaches. The numeric results show that the proposed combination approach can help classifiers to achieve better performance.
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