Class Imbalance and Data Irregularities in Classification

This chapter delves into the critical challenges of data irregularities and class imbalance in machine learning classification, exploring issues such as missing, incomplete, inconsistent, and noisy features, and discussing strategies for feature normalization. It addresses the pervasive problems of overfitting and underfitting, highlighting their impact on model performance. Furthermore, the chapter examines how class imbalance and data irregularities affect multiclass, multiobjective, and multilabel classification, offering practical solutions to mitigate these issues and improve the robustness and accuracy of classification models.