Title: A one-class classification approach based on SVDD for imbalanced and overlapping classes

Authors: SeyyedMohammad JavadiMoghaddam; Reyhane Rateghi

Addresses: Department of Computer, Faculty of Engineering, Bozorgmehr University of Qaenat, Iran ' Department of Computer Engineering, Azad University of Birjand, Iran

Abstract: Imbalanced data classification is a challenge especially when there is overlapping between two classes. The overlap makes it almost impossible to create differences in the two classes and isolate them. In the real world, many of the datasets are imbalanced and overlapped. This paper identifies the overlapping regions optimally by comparing the results of a single-class support vector data description (SVDD) algorithm performed on each class. Then, the method uses the nearest-neighbour algorithm to classify the data in the overlapping region. The result of the evaluation on the datasets with a high imbalanced rate shows better performance than other approaches.

Keywords: classification; imbalanced data; overlapping; SVDD algorithm; KNN algorithm.

DOI: 10.1504/IJDATS.2021.120112

International Journal of Data Analysis Techniques and Strategies, 2021 Vol.13 No.4, pp.291 - 301

Received: 03 Jul 2020
Accepted: 30 Mar 2021
Published online: 07 Jan 2022 *

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