Learning from Imbalanced Classes

Learning from Imbalanced Classes

August 25th, 2016 If you’re fresh from a machine learning course, chances are most of the datasets you used were fairly easy. Among other things, when you built classifiers, the example classes were balanced, meaning there were approximately the same number of examples of each class. Instructors usually employ cleaned up datasets so as to concentrate on teaching specific algorithms or techniques without getting distracted by other issues. Usually you’re shown examples like the figure below in two dimensions, with points representing examples and different colors (or shapes) of the points representing the class: The goal of a classification algorithm is to attempt to learn a separator (classifier) that can distinguish the two. There are many ways of doing this, based on various mathematical, statistical, or geometric assumptions: But when…


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