Majority clustering for imbalanced image classification.

ABSTRACT: Class imbalance is a prevalent challenge in image classification tasks, where certain classes are significantly underrepresented compared to others. This imbalance often leads to biased models that perform poorly in predicting minority classes, affecting the overall performance and reliability of image classification systems. In this article, an under-sampling approach based on reducing the samples of majority class is used along with the unsupervised clustering approach for partitioning the majority class into clusters within the datasets. The proposed technique, Majority Clustering for Imbalanced Image Classification (MCIIC) improves the traditional binary classification problems by converting it into multi-class problem, thereby creating the more balanced classification solution to the

SUBMITTER: Sharma K 

PROVIDER: S-EPMC12453654 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature