Investigating the Effect of Sampling Methods for Imbalanced Data Distributions

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Abstract
Classification is an important and well-known technique in the field of machine learning, and the training data will significantly influence the classification accuracy. However, the training data in real-world applications often are imbalanced class distribution. It is important to select the suitable training data for classification in the imbalanced class distribution problem. In this paper, we propose a cluster-based sampling approach for selecting the representative data as training data to improve the classification accuracy and investigate the effect of under-sampling methods in the imbalanced class distribution problem. In the experiments, we evaluate the performances for our cluster-based sampling approach and the other sampling methods in the previous studies.

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