Hybrid classification approach for imbalanced datasets

Date
2015-01-01
Authors
Gao, Tianxiang
Journal Title
Journal ISSN
Volume Title
Publisher
Altmetrics
Authors
Research Projects
Organizational Units
Journal Issue
Series
Abstract

The research area of imbalanced dataset has been attracted increasing attention from both academic and industrial areas, because it poses a serious issues for so many supervised learning problems. Since the number of majority class dominates the number of minority class are from minority class, if training dataset includes all data in order to fit a classic classifier, the classifier tends to classify all data to majority class by ignoring minority data as noise. Thus, it is very significant to select appropriate training dataset in the prepossessing stage for classification of imbalanced dataset. We propose an combination approach of SMOTE (Synthetic Minority Over-sampling Technique) and instance selection approaches. The numeric results show that the proposed combination approach can help classifiers to achieve better performance.

Description
Keywords
Industrial Engineering
Citation
Source