@inproceedings{38cd1d18bb064639a8210d3fa9556804,
title = "Extending sample information for small data set prediction",
abstract = "This paper proposes a method that focuses on creating new data attributes by using fuzzy operations for solving small dataset learning problems. Using the idea of fuzzy rules, the membership value of antecedents in each rule can be extracted from the data point. Therefore, in this research, those membership values will be deemed as new data features and the data dimensionality will be extended. To test the effectiveness of the proposed method, the data set with new data features and the one with no special treatment will be utilized respectively to build predictive models. Paired t-test is carried out to see how effective the proposed method can improve the learning on the basis of small sample sets.",
author = "Chen, {Hung Yu} and Li, {Der Chiang} and Lin, {Liang Sian}",
year = "2016",
month = aug,
day = "31",
doi = "10.1109/IIAI-AAI.2016.16",
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 = "710--714",
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",
note = "5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 ; Conference date: 10-07-2016 Through 14-07-2016",
}