Enhanced Classification to Counter the Problem of Cluster Disjuncts

Provided by: IJCTT-International Journal of Computer Trends and Technology
Topic: Data Management
Format: PDF
In this paper, the authors preset a rigorous yet practical model dubbed as Cluster Disjunct Minority Oversampling TEchnique (CDMOTE) for learning from skewed training data. This algorithm provides a simpler and faster alternative by using cluster disjunct concept. They conduct experiments using fifteen UCI data sets from various application domains using five algorithms for comparison on six evaluation metrics. The empirical study suggests that CDMOTE have been believed to be effective in addressing the class imbalance problem.

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