Title: A PRIORI SYNTHETIC SAMPLING FOR INCREASING CLASSIFICATION SENSITIVITY IN IMBALANCED DATA SETS
نویسنده
چکیده
Class imbalance data usually suffers from data intrinsic properties beyond that of imbalance alone. The problem is intensified with larger levels of imbalance most commonly found in observational studies. Extreme cases of class imbalance are commonly found in many domains including fraud detection, mammography of cancer and post term births. These rare events are usually the most costly or have the highest level of risk associated with them and are therefore of most interest.
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