Over-Sampling via Under-Sampling in Strongly Imbalanced Data

Classification of imbalanced data sets is an important challenge in machine learning. Whenever the size of one of the classes is very smaller than others, it is called as imbalanced data sets. In these types of data sets, all the algorithms and efficiency criteria care the majority class and ignore the minority class. But sometimes these mi-nority class contains important information and ignoring that information may be cause wrong total results, especially whenever, the accuracy of results are very important such as medical data. Therefore, proposing approaches for solving this problem is necessary. One of the best approaches to solve this problem is re-sampling. Re-sampling runs usu-ally as an extra preprocessing step and it has two main methods named as over-sampling and under-sampling. This investigation analysis the effect of Ratio imbalance and the selected classifier on the application of several re-sampling strategies to deal with imbalanced data sets.

Classification; Imbalanced Data; Minority Class; Re-Sampling; Over-Sampling; Under-Sampling