Extending Sample Information for Small Data Set Prediction

被引：10

作者：

Chen, Hung-Yuj ^{[1
]}

Li, Der-Chiang ^{[2
]}

Lin, Liang-Sian ^{[3
]}

机构：

[1] Natl Cheng Kung Univ, Dept Informat Management, Tainan, Taiwan

[2] Natl Cheng Kung Univ, Dept Ind & Informat Management, Tainan, Taiwan

[3] Ind Technol Res Inst, Informat & Commun Res Labs, Hsinchu, Taiwan

来源：

PROCEEDINGS 2016 5TH IIAI INTERNATIONAL CONGRESS ON ADVANCED APPLIED INFORMATICS IIAI-AAI 2016 | 2016年

关键词：

small dataset learning; extended data attributes; box-chart-based domain estimation; support vector regression; CLUSTER-ANALYSIS; PERFORMANCE; DIFFUSION; NETWORK;

D O I：

10.1109/IIAI-AAI.2016.16

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：710 / 714

页数：5

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