Under the increasing pressure of global competition, product life cycles are becoming shorter and shorter. This means that better methods are needed to analyse the limited information obtained at the trial stage in order to derive useful knowledge that can aid in mass production. Machine learning algorithms, such as data mining techniques, are widely applied to solve this problem. However, a certain amount of training samples is usually required to determine the validity of the information that is obtained. This study uses only a few data points to estimate the range of data attribute domains using a data diffusion method, in order to derive more useful information. Then, based on practical engineering experience, we generate virtual samples with a noise disturbance method to improve the robustness of the predictions derived from a multiple linear regression. One real data set obtained from a large TFT-LCD company is examined in the experiment, and the results show the proposed approach to be effective.
All Science Journal Classification (ASJC) codes
- Strategy and Management
- Management Science and Operations Research
- Industrial and Manufacturing Engineering