Generative Oversampling Method for Imbalanced Data on Bearing Fault Detection and Diagnosis
نویسندگان
چکیده
منابع مشابه
Generative Oversampling for Mining Imbalanced Datasets
One way to handle data mining problems where class prior probabilities and/or misclassification costs between classes are highly unequal is to resample the data until a new, desired class distribution in the training data is achieved. Many resampling techniques have been proposed in the past, and the relationship between resampling and cost-sensitive learning has been well studied. Surprisingly...
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Classification problem for imbalanced datasets is pervasive in a lot of data mining domains. Imbalanced classification has been a hot topic in the academic community. From data level to algorithm level, a lot of solutions have been proposed to tackle the problems resulted from imbalanced datasets. SMOTE is the most popular data-level method and a lot of derivations based on it are developed to ...
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Many classifiers are designed with the assumption of wellbalanced datasets. But in real problems, like protein classification and remote homology detection, when using binary classifiers like support vector machine (SVM) and kernel methods, we are facing imbalanced data in which we have a low number of protein sequences as positive data (minor class) compared with negative data (major class). A...
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Many classifiers are designed with the assumption of wellbalanced datasets. But in real problems, like protein classification and remote homology detection, when using binary classifiers like support vector machine (SVM) and kernel methods, we are facing imbalanced data in which we have a low number of protein sequences as positive data (minor class) compared with negative data (major class). A...
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ژورنال
عنوان ژورنال: Applied Sciences
سال: 2019
ISSN: 2076-3417
DOI: 10.3390/app9040746