Imbalanced data preprocessing techniques utilizing local data characteristics

TL;DR

This paper reviews and proposes new data resampling techniques that leverage local data characteristics to better address class imbalance in machine learning, especially in complex multi-class and histopathological data contexts.

Contribution

It introduces novel resampling strategies that utilize both minority and majority class information, improving upon traditional methods like SMOTE.

Findings

Enhanced resampling strategies outperform traditional methods.

Effective in multi-class and histopathological data classification.

Addresses limitations of existing imbalance techniques.

Abstract

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.