Class Imbalance Learning Methods for Support Vector Machines
Rukshan Batuwita, Vasile Palade · 2013
Support vector machines (SVMs) is a very popular machine learning technique. An SVM classifier trained on an imbalanced dataset can produce suboptimal models that are biased toward the majority class and have low performance on the minority class, as most of the other classification paradigms. There have been various data preprocessing and algorithmic techniques proposed in the literature to alleviate this problem for SVMs. This chapter aims to review these techniques. It briefly reviews the learning algorithm of SVMs. Next, it discusses why SVMs are sensitive to the imbalance in datasets. Finally, the chapter presents the existing techniques proposed in the literature to handle the class imbalance problem for SVMs. Controlled Vocabulary Terms learning (artificial intelligence)