A-SMOTE: A New Preprocessing Approach for Highly Imbalanced Datasets by Improving SMOTE
نویسندگان
چکیده
منابع مشابه
Preprocessing noisy imbalanced datasets using SMOTE enhanced with fuzzy rough prototype selection
The Synthetic Minority Over Sampling TEchnique (SMOTE) is a widely used technique to balance imbalanced data. In this paper we focus on improving SMOTE in the presence of class noise. Many improvements of SMOTE have been proposed, mostly cleaning or improving the data after applying SMOTE. Our approach differs from these approaches by the fact that it cleans the data before applying SMOTE, such...
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-The class imbalanced problem occurs in various disciplines when one of target classes has a small number of instances compare to other classes. A classifier normally ignores or neglects to detect a minority class due to the small number of class instances. It poses a challenge to any classifier as it becomes hard to learn the minority class samples. Most of the oversampling methods may generat...
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Classification of imbalanced datasets is a challenging task for standard algorithms. Although many methods exist to address this problem in different ways, generating artificial data for the minority class is a more general approach compared to algorithmic modifications. SMOTE algorithm and its variations generate synthetic samples along a line segment that joins minority class instances. In th...
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Machine learning approach has got major importance when distribution of data is unknown. Classification of data from the data set causes some problem when distribution of data is unknown. Characterization of raw data relates to whether the data can take on only discrete values or whether the data is continuous. In real world application data drawn from non-stationary distribution, causes the pr...
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ژورنال
عنوان ژورنال: International Journal of Computational Intelligence Systems
سال: 2019
ISSN: 1875-6883
DOI: 10.2991/ijcis.d.191114.002