Quality Control by Supervised Learning Method

• Main Authors: Yu, Ching-Hsiang, 游景翔
• Other Authors: 周珮婷
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/s8c6c2

碩士 === 國立政治大學 === 統計學系 === 106 === The purpose of the current study was to explore the assumptions, features, and acceptance process of acceptance sampling plan in traditional Incoming Quality Control (IQC). Four features were proposed to describe distributions of data. Supervised machine learning models, Support Vector Machine(SVM), Logistic Regression, and Random Forest, were applied for detection of fraud. The results showed that the proposed features can effectively differentiate between real and fake datasets. The techniques can be used in future for supplier selection and evaluation. The identification of appraisal cost will be reduced and a triple-win situation for suppliers, retailers, and customers can be created.