Surrounding neighborhood-based SMOTE for learning from imbalanced data sets
نویسندگان
چکیده
منابع مشابه
Addressing data complexity for imbalanced data sets: analysis of SMOTE-based oversampling and evolutionary undersampling
In the classification framework there are problems in which the number of examples per class is not equitably distributed, formerly known as imbalanced data sets. This situation is a handicap when trying to identify the minority classes, as the learning algorithms are not usually adapted to such characteristics. An usual approach to deal with the problem of imbalanced data sets is the use of a ...
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ژورنال
عنوان ژورنال: Progress in Artificial Intelligence
سال: 2012
ISSN: 2192-6352,2192-6360
DOI: 10.1007/s13748-012-0027-5