A preprocessing method combined with an ensemble framework for the multiclass imbalanced data classification

Abstract

Skewed distributions appear in many real-world classification problems. Skewed distributions, underrepresented classes, and multiple overlapping regions in multiclass imbalanced datasets deteriorate the performance of existing classification algorithms and approaches. In this context, we combine a novel preprocessing procedure to tackle minority classes in multiclass imbalanced problems with an ensemble framework. The preprocessing method oversamples the minority classes based on normalized probability, and then an ensemble called a stacked generalization framework is used to train the model. The motive behind combining the ensemble framework and the preprocessing procedure is to enhance the overall classification performance of the classifier for multiclass imbalanced problems. Experimental results on 20 multiclass imbalanced datasets show that the proposed preprocessing method with the ensemble framework outperforms the representative approaches in 13 datasets for macro average arithmetic (MAvA) and mean F-measure (MFM) metrics. In the case of state-of-the-art techniques, the proposed approach steered 14 datasets for the MAvA metric and 15 datasets for the MFM metric to success.

KEYWORDS:

• Imbalanced dataset
• multiclass imbalance classification
• oversampling
• multiple classifier system
• ensemble learning

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

M. R. Pavan Kumar

Pavan Kumar M R received M. Tech degree from VIT University, Vellore, Tamilnadu. Currently, he is a research scholar in the School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamilnadu, India and pursuing his Ph.D. degree in the field of Data Analytics. His main areas of research include Big Data Analytics, Machine Learning, and Deep Learning.

Prabhu Jayagopal

Dr. Prabhu J received his Ph.D. (Computer science and Engineering) and Master of Computer Science and Engineering from Sathyabama University, Chennai, Tamil Nadu, India. He received a B.Tech degree from the Department of Information Technology, Vellore Engineering College, Affiliated to Madras University in 2004 located at Vellore, Tamil Nadu, India. He worked as an Assistant Professor in various Engineering colleges for more than 13 years. Now he is working as an Associate Professor in the School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India. His research interests are Software Testing, Software Quality Assurance, Software Metrics, and Big Data.