Class Imbalance Problem

A Class Imbalance Problem is a machine learning classification task problem that occurs when using highly imbalanced datasets

• See: Supervised Classification Task, Imbalanced Distribution, Class Distribution, Skewed Training Dataset.

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References

2017

• (Ling & Sheng, 2017) ⇒ C.X., Sheng V.S. (2017) "Class Imbalance Problem". In: Sammut C., Webb G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA
  • QUOTE: Data are said to suffer the Class Imbalance Problem when the class distributions are highly imbalanced. In this context, many classification learning algorithms have low predictive accuracy for the infrequent class. Cost-sensitive learning is a common approach to solve this problem .

    (...) Class imbalanced datasets occur in many real-world applications where the class distributions of data are highly imbalanced. For the two-class case, without loss of generality, one assumes that the minority or rare class is the positive class, and the majority class is the negative class. Often the minority class is very infrequent, such as 1 % of the dataset. If one applies most traditional (cost-insensitive) classifiers on the dataset, they are likely to predict everything as negative (the majority class). This was often regarded as a problem in learning from highly imbalanced datasets.

2011

• (Ling & Sheng, 2011) ⇒ Charles X. Ling; Victor S. Sheng. (2011). “Class Imbalance Problem.” In: (Sammut & Webb, 2011) p.167

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