Cog BUS– Center of Gravity Based Under Sampling Method for Imbalanced Data Classification
Shidha M V1, T Mahalekshmi2, Sabu M K3
1Shidha M V, Research Scholar, Bharathiar University, Coimbatore, India.
2T Mahalekshmi, Professor & Principal, SNIT, Kollam, India.
3Sabu M K, Associate Professor, Department of Computer Applications, CUSAT, India.
Manuscript received on 04 March 2019 | Revised Manuscript received on 09 March 2019 | Manuscript published on 30 July 2019 | PP: 2463-2468 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2077078219/19©BEIESP | DOI: 10.35940/ijrte.B2077.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Learning of class imbalanced data becomes a challenging issue in the machine learning community as all classification algorithms are designed to work for balanced datasets. Several methods are available to tackle this issue, among which the resampling techniques- undersampling and oversampling are more flexible and versatile. This paper introduces a new concept for undersampling based on Center of Gravity principle which helps to reduce the excess instances of majority class. This work is suited for binary class problems. The proposed technique –CoGBUS- overcomes the class imbalance problem and brings best results in the study. We take F-Score, GMean and ROC for the performance evaluation of the method.
Index Terms: Center of Gravity, F-Score, GMean, ROC, Undersampling.
Scope of the Article: Classification