A New Data Transformation Model for Small Dataset Learning

  • 溫 怡翔

Student thesis: Doctoral Thesis


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 datasets 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 datasets Accordingly this research proposes the Small-Johnson Data Transformation (SJDT) method to transform small raw data to normal distributions to generate virtual samples When compared with four other methods the results obtained with a real small dataset drawn from the Film Transistor Liquid Crystal Display (TFT-LCD) industry in Taiwan demonstrate that the proposed method is able to effectively improve the forecasting ability with small sample sizes
Date of Award2016 Aug 5
Original languageEnglish
SupervisorDer-Chiang Li (Supervisor)

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