SROT: Sparse representation-based over-sampling technique for classification of imbalanced dataset
نویسندگان
چکیده
منابع مشابه
Borderline over-sampling for imbalanced data classification
Traditional classification algorithms, in many times, perform poorly on imbalanced data sets in which some classes are heavily outnumbered by the remaining classes. For this kind of data, minority class instances, which are usually much more of interest, are often misclassified. The paper proposes a method to deal with them by changing class distribution through oversampling at the borderline b...
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Abstract—Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalance...
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of the Thesis Classification of Imbalanced Data Using Synthetic Over-Sampling Techniques
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ژورنال
عنوان ژورنال: IOP Conference Series: Earth and Environmental Science
سال: 2017
ISSN: 1755-1307,1755-1315
DOI: 10.1088/1755-1315/81/1/012201