Majority clustering for imbalanced image classification

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 problems where one need to detect rare samples present in the dataset. By utilizing the elbow method, we determine the optimal number of clusters for the majority class and assign each cluster a new class label. This complete process ensures a balanced and symmetrical class distribution, effectively addressing imbalances both between and within classes and helps to perform imbalanced classification. The effectiveness of the proposed model is evaluated on various benchmark datasets, demonstrating their ability to improve the predictive performance of the proposed MCIIC on imbalanced image datasets. Through empirical evaluation, we showcase the impact of proposed technique on model accuracy, precision, recall, and F1-score, highlighting its importance as a pre-processing step in handling imbalanced image datasets. The results highlight the significance of proposed model as a practical approach to address the challenges posed by imbalanced data distributions in machine learning tasks.