Medicine and Health Sciences Commons^™

Oversampling Methods For Imbalanced Dataset Classification And Their Application To Gynecological Disorder Diagnosis, Iman Nekooeimehr

USF Tampa Graduate Theses and Dissertations

In many applications, the dataset for classification may be highly imbalanced where most of the instances in the training set may belong to some of the classes (majority classes), while only a few instances are from the other classes (minority classes). Conventional classifiers will strongly favor the majority class and ignore the minority instances. The imbalance problem can occur in both binary data classification and also in ordinal regression. Ordinal regression is a supervised approach for learning the ordinal relationship between classes. Extensive research has been performed for addressing imbalanced datasets for binary classification; however, current methods do not address …