Imbalanced data preprocessing techniques utilizing local data characteristics
This work tackles data imbalance issues for machine learning practitioners, particularly in medical domains, but appears incremental as it builds on existing SMOTE-based techniques.
The paper addresses the challenge of data imbalance in machine learning by developing novel data resampling strategies that utilize information from both minority and majority classes, applied to histopathological data classification.
Data imbalance, that is the disproportion between the number of training observations coming from different classes, remains one of the most significant challenges affecting contemporary machine learning. The negative impact of data imbalance on traditional classification algorithms can be reduced by the data preprocessing techniques, methods that manipulate the training data to artificially reduce the degree of imbalance. However, the existing data preprocessing techniques, in particular SMOTE and its derivatives, which constitute the most prevalent paradigm of imbalanced data preprocessing, tend to be susceptible to various data difficulty factors. This is in part due to the fact that the original SMOTE algorithm does not utilize the information about majority class observations. The focus of this thesis is development of novel data resampling strategies natively utilizing the information about the distribution of both minority and majority class. The thesis summarizes the content of 12 research papers focused on the proposed binary data resampling strategies, their translation to the multi-class setting, and the practical application to the problem of histopathological data classification.