Breast Cancer Diagnosis Using Cluster-based Undersampling and Boosted C5.0 Algorithm


Learning from imbalanced data set is relatively new challenge for breast cancer diagnosis, where the diseases cases are often quite rare relative to normal population. Although traditional algorithms are all accuracy-oriented which result biased towards the majority class. The combinations of sampling methods with ensemble classifiers have shown certainly good performance. In this paper, a hybrid of cluster-based undersampling and boosted C5.0 is proposed. The proposed classification model consists of two phases: cluster analysis and classification. In cluster analysis, affinity propagation algorithm is used to define the number of clusters, and then the k-means clustering is utilized to select the border and informative samples. In the classification phase, C5.0 algorithm is used in conjunction with boosting technical, owing to leverage the strength of the individual classifiers. The proposed algorithm is assessed by 14 benchmark imbalanced data sets taken from UCI dataset repository. The extensive experimental results on different imbalanced datasets demonstrated that the proposed algorithm can achieve better classification performance in terms of Matthews’ Correlation Coefficient (MCC) as compared to other existing imbalanced dataset classification algorithms.

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Corresponding author

Correspondence to Li Chen.

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Recommended by Associate Editor M. Jaead Khan under the direction of Editor Doo Yong Lee. This work is supported by the National Natural Science Foundation of China (61966029, 72061030), Shaanxi Technology Committee industrial Public Relations Projection (2018GY-146), Shaanxi Provincial Department of Education Scientific Research Project (19JK0996), Yulin City Science and Technology Bureau (2019-93-3, 2019-77-3) and Yulin University (14YK37).

Jue Zhang received her M.S. degree in software engineering from Xidian University, Xi’an, China, in 2010. Now, she is pursuing her Ph.D. degree in Northwest Univrsity. Her research interests include machine learning, data mining, and imbalanced data classification.

Li Chen received her Ph.D. degree from Xidian University, Xi’an, China, in 2003. She is a Professor and Doctoral Supervisor of the School of Information Science and Technology, at Northwest University, Xi’an, China. Her research interests include data minging, intelligent information processing and intelligent control of big data. She has authored and co-authored over 100 papers in journals and conference proceedings.

Jian-xue Tian received his M.S. degree in University of Electronic Science and Technology of China, Chengdu, Sichuan Province, China, in 2014. His current research interests include fuzzy system and intelligent control.

Fazeel Abid received his Ph.D. degree in Northwest University of China, Xi’an, in 2020. He was a visiting internship student from Pakistan. His current research interests include sentiment analysis, social network and web mining in the aspect of deep learning.

Wusi Yang received his Ph.D. degree in Northwest University of China, Xi’an, in 2020. He is currently a lecturer in Xianyang Normal University. His current research interests focus on machine learning, swarm intelligence optimization, and multi-objective optimization.

Xiao-fen Tang received her Ph.D. degree in computer science from Northwest University in Xi’an, China. She is currently an associate professor in Ningxia University. Her research interest is mainly in the area of neural network and bioinformatics. She has published several research papers in scholarly journals in the above research areas.

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Zhang, J., Chen, L., Tian, Jx. et al. Breast Cancer Diagnosis Using Cluster-based Undersampling and Boosted C5.0 Algorithm. Int. J. Control Autom. Syst. (2021).

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  • Breast cancer diagnosis
  • cluste analysis
  • imbalanced data classification
  • sample selection
  • undersampling