Using Goodness-of-fit Techniques to Select Virtual Samples to Improve Small Data of Cost Prediction

Abstract

After the transition of consumer demand in the market in order to create the technology and develop the finance corporations adopt industrial transformation strategy to make an alteration of the corporation structure and scale and improve the competition and satisfy customer needs Under stress of time and cost the cost prediction in the product development process is often being conducted without acquiring sufficient sample data The method of constructing prediction model to perform cost prediction analysis from limited data in the competitive and qualitative environment has become an important goal for corporation management For the issue of insufficient sample data in the practice and application the matter of how to develop the meaningful information from few samples is the research topic of small data set learning Many studies noted that in order to address the predict issues of production system and break through the learning efficiency when data is inadequate small data research increase the sample amount by virtual sample generation method to increase the sample data through small data sets and enhance the accuracy of prediction learning The study adopted methods of two parameters Weibull distribution and Maximal P-value approach to generate virtual sample to increase data The confounding factors were filtered and screened by Kolmogorov–Smirnov test and imported the training sample data into the prediction learning instruction to compare the accuracy of prediction in different methods The study adopted small data sets of automotive printed circuit board in Taiwan as experiment case The result indicated the method conducted in the study had a significant improvement on the prediction error than one in original data and it received better effect of prediction comparing to maximum p-value

Date of Award	2015 Nov 9
Original language	English
Supervisor	Der-Chiang Li (Supervisor)