@inproceedings{0db916302ef34c189750b47441dbcf00,
title = "A learning approach with under-and over-sampling for imbalanced data sets",
abstract = "It is difficult for learning models to achieve high classification performance with imbalanced data sets. To conquer the problem, this study presents a strategy involving the reduction of size of majority data set and the generation of synthetic samples of minority data set. Parkinson's disease data set is used to examine and to compare the performance of classification methods. The paired t-tests are also used to show the effectiveness of the proposed method comparing with that of the other methods.",
author = "Yeh, {Chun Wu} and Li, {Der Chiang} and Lin, {Liang Sian} and Tsai, {Tung I.}",
note = "Funding Information: This study was supported by the Ministry of Science and Technology under Grant MOST 104-2410-H-168-003. Publisher Copyright: {\textcopyright} 2016 IEEE.; 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 ; Conference date: 10-07-2016 Through 14-07-2016",
year = "2016",
month = aug,
day = "31",
doi = "10.1109/IIAI-AAI.2016.20",
language = "English",
series = "Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "725--729",
editor = "Ayako Hiramatsu and Tokuro Matsuo and Akimitsu Kanzaki and Norihisa Komoda",
booktitle = "Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016",
address = "United States",
}