Oversampling Methods for Imbalanced Dataset Classification and their Application to Gynecological Disorder Diagnosis
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 t...
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| Format: | Others |
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Scholar Commons
2016
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| Online Access: | http://scholarcommons.usf.edu/etd/6335 http://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=7531&context=etd |