Degree Type
Thesis
Date of Award
2015
Degree Name
Master of Science
Department
Industrial and Manufacturing Systems Engineering
First Advisor
Sigurdur Olafsson
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.
DOI
https://doi.org/10.31274/etd-180810-3884
Copyright Owner
Tianxiang Gao
Copyright Date
2015
Language
en
File Format
application/pdf
File Size
43 pages
Recommended Citation
Gao, Tianxiang, "Hybrid classification approach for imbalanced datasets" (2015). Graduate Theses and Dissertations. 14331.
https://lib.dr.iastate.edu/etd/14331
