A New Under-Sampling Method to Face Class Overlap and Imbalance

• Main Authors: Angélica Guzmán-Ponce, Rosa María Valdovinos, José Salvador Sánchez, José Raymundo Marcial-Romero
• Format: Article
• Language: English
• Published: MDPI AG 2020-07-01
• Series: Applied Sciences
• Subjects: class imbalance; class overlap; under-sampling; clustering; DBSCAN; minimum spanning tree
• Online Access: https://www.mdpi.com/2076-3417/10/15/5164

Class overlap and class imbalance are two data complexities that challenge the design of effective classifiers in Pattern Recognition and Data Mining as they may cause a significant loss in performance. Several solutions have been proposed to face both data difficulties, but most of these approaches tackle each problem separately. In this paper, we propose a two-stage under-sampling technique that combines the DBSCAN clustering algorithm to remove noisy samples and clean the decision boundary with a minimum spanning tree algorithm to face the class imbalance, thus handling class overlap and imbalance simultaneously with the aim of improving the performance of classifiers. An extensive experimental study shows a significantly better behavior of the new algorithm as compared to 12 state-of-the-art under-sampling methods using three standard classification models (nearest neighbor rule, J48 decision tree, and support vector machine with a linear kernel) on both real-life and synthetic databases.