A Novel approach to Handle Imbalanced Dataset in Machine Learning

Taj Sapra, Shubhama, S. Meena
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Abstract

The world has seen an exponential rise in machine learning and artificial intelligence since the 1990s. We apply machine learning models to solve various real life problems like regression and classification. However, class imbalance is a very common issue faced for classification problems in machine learning. In this study, we propose new greedy resampling techniques to solve the problem of class imbalance. We shall also compare the results of these techniques with the Synthetic Minority Over-sampling Technique (SMOTE).
机器学习中一种处理不平衡数据集的新方法
自20世纪90年代以来,世界上的机器学习和人工智能呈指数级增长。我们应用机器学习模型来解决各种现实生活中的问题,比如回归和分类。然而,在机器学习分类问题中,类不平衡是一个非常常见的问题。在本研究中,我们提出新的贪婪重采样技术来解决类不平衡问题。我们还将这些技术的结果与合成少数过采样技术(SMOTE)进行比较。
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