COSM: Controlled Over-Sampling Method
A Methodological Proposal to Overcome the Class Imbalance Problem in Data Mining
DOI:
https://doi.org/10.14738/tmlai.82.7925Keywords:
Class imbalance problem, Data Mining, Holdout Method, Oversampling, Rare Class Mining, UndersamplingAbstract
The class imbalance problem is widespread in Data Mining and it can reduce the general performance of a classification model. Many techniques have been proposed in order to overcome it, thanks to which a model able to handling rare events can be trained. The methodology presented in this paper, called Controlled Over-Sampling Method (COSM), includes a controller model able to reject new synthetic elements for which there is no certainty of belonging to the minority class. It combines the common Machine Learning method for holdout with an oversampling algorithm, for example the classic SMOTE algorithm. The proposal explained and designed here represents a guideline for the application of oversampling algorithms, but also a brief overview on techniques for overcoming the problem of the unbalanced class in Data Mining.
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