Abstract

Imbalanced datasets play an important role in many fields in real applications such as medical diagnosis, business risk management, abnormal product testing and evaluation. In these cases, the minority classes are usually important. Granular computing has been developed and effectively applied to many problems especially imbalanced data classification. In this paper, we propose a new strategy to build information granulations (IGs) for each class separately and represent sub-attributes based on categorical values (including discretized values of the numerical attributes) to solve the overlapping among IGs. This strategy reduces the computational time, improves classification performance and considers high-balanced accuracy among classes. The experimental results on several datasets have demonstrated the effectiveness of our proposal.

Details

Title
Information granulation construction and representation strategies for classification in imbalanced data based on granular computing
Author
Lai, Duc Anh 1 ; Bay Vo 2 ; Pedrycz, Witold 3 

 Department of Education and Training of Long An Province, Long An, Vietnam 
 Faculty of Information Technology, Ho Chi Minh City University of Technology, Ho Chi Minh, Vietnam 
 Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada 
End page
126
Publication year
2017
Publication date
Jun 2017
Publisher
Taylor & Francis Ltd.
ISSN
24751839
e-ISSN
24751847
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2363181276
Copyright
© 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.