Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification

Imbalanced class distributions pose a prevalent challenge in numerous classification problems, requiring effective strategies for learning from such skewed data. Traditional machine learning algorithms often struggle with imbalanced datasets, as they tend to bias their classification functions toward the majority class, resulting in suboptimal performance for minority classes. In our research, we propose a novel approach to address this challenge specifically tailored for Support Vector Machines (SVM), a well-established family of learning algorithms. Our method leverages a kernel trick to enhance the SVM's classification capabilities on imbalanced datasets named KTI. It aims to streamline the classification process by incorporating adaptive data transformations within the algorithm itself, offering a more efficient and integrated solution for handling imbalanced data. Experimental evaluations conducted on diverse real-world datasets demonstrate the superior performance of our proposed strategy compared to existing methods, showcasing its potential for practical applications in classification tasks with skewed class distributions.