SOM-US: A Novel Under-Sampling Technique for Handling Class Imbalance Problem

SOM-US: A Novel Under-Sampling Technique for Handling Class Imbalance Problem

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A significant research challenge in data mining and machine learning is class imbalance classification since the majority of real-world datasets are imbalanced.When the dataset is highly unbalanced, the us polo assn mens sweaters majority of available classification techniques frequently underperform on minority-class cases.This is due to the fact that they disregard the relative distribution of each class in favor of maximizing the overall accuracy.

Various techniques based on sampling methods, cost-sensitive learning, and ensemble methods have recently been employed to handle the class imbalance problem.This paper proposes a michael harris sunglasses new clustering-based under-sampling (US) technique, called SOM-US, for handling the class imbalance problem using the self-organized map (SOM).To validate the proposed approach, an experimental study was conducted to improve the capability of a classifier-logistic regression for software defect prediction by applying SOM-US over a NASA software defect dataset.

The proposed approach was compared with six existing under-sampling methods on two performance measures.The results demonstrate that the SOM-US significantly improves the prediction capability of logistic regression over other under-sampling techniques for software defect prediction.