A novel data transformation model for small data-set learning
| Autor: | Wen-Chih Chen, I-Hsiang Wen, Der-Chiang Li |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
Engineering Statistical assumption Strategy and Management media_common.quotation_subject Data transformation (statistics) Sample (statistics) 02 engineering and technology Management Science and Operations Research Machine learning computer.software_genre Industrial and Manufacturing Engineering Set (abstract data type) 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Quality (business) media_common Small data business.industry Sample size determination 020201 artificial intelligence & image processing Johnson's algorithm Artificial intelligence Data mining business computer |
| Zdroj: | International Journal of Production Research. 54:7453-7463 |
| ISSN: | 1366-588X 0020-7543 |
| DOI: | 10.1080/00207543.2016.1192301 |
| Popis: | In most highly competitive manufacturing industries, the sample sizes are usually very small in pilot runs, in order to quickly launch new products. However, it is always difficult for engineers to improve the quality in mass production runs based on the limited data obtained in this way. Past research has demonstrated that adding artificial samples can be an effective approach when learning with small data-sets. However, a prior analysis of the data is needed to deduce the appropriate sample distributions within which the artificial samples are generated. Johnson transformation is one of the well-known models that can be applied to bring data close to a normal distribution with the satisfaction of certain statistical assumptions. The sample size required for such data transformation methods is usually large, and this thus motivates the efforts of the current study to develop a new method which is suitable for small data-sets. Accordingly, this research proposes the small Johnson Data Transformation metho... |
| Databáze: | OpenAIRE |
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