Generating Synthetic Samples to Improve Small Sample Learning with Mixed Numerical and Categorical Attributes

Yao San Lin, Wan Ni Cheng, Chien Chih Chen, Der Chiang Li, Hung Yu Chen

研究成果: Conference contribution

摘要

The small data learning issue has existed for over one hundred years (since 1908) when the Student's t-distribution was first developed. Few statistical tools can evaluate a population appropriately if the sample size is too small; small samples can be remedied through virtual sample generation (VSG) methods, which are widely used in industry and machine learning. However, most VSG methods were developed for data having only numerical attributes, very few studies have dealt with nominal attributes and cause domain estimation limitations. Therefore, this paper proposes a method that generates virtual samples based on the discrete degrees of nominal attributes, and then estimates the general population domains by fuzzy membership functions. A backpropagation neural network model and a support vector regression model are used to test the efficiency of the proposed method, while the Wilcoxon-sign test is used to test the difference with raw data sets. The result shows that the proposed method can reduce the mean absolute error and enhance classification accuracy by generating virtual samples that have nominal attributes.

原文English
主出版物標題Proceedings - 2019 8th International Congress on Advanced Applied Informatics, IIAI-AAI 2019
發行者Institute of Electrical and Electronics Engineers Inc.
頁面567-572
頁數6
ISBN(電子)9781728126272
DOIs
出版狀態Published - 2019 七月
事件8th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2019 - Toyama, Japan
持續時間: 2019 七月 72019 七月 11

出版系列

名字Proceedings - 2019 8th International Congress on Advanced Applied Informatics, IIAI-AAI 2019

Conference

Conference8th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2019
國家/地區Japan
城市Toyama
期間19-07-0719-07-11

All Science Journal Classification (ASJC) codes

  • 電腦網路與通信
  • 電腦科學應用
  • 資訊系統
  • 資訊系統與管理
  • 社會科學(雜項)

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