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http://dx.doi.org/10.6109/jkiice.2021.25.6.749

The methods to improve the performance of predictive model using machine learning for the quality properties of products  

Kim, Jong Hoon (Applied Data Science, Sungkyunkwan University)
Oh, Hayoung (College of Computing & Informatics, Sungkyunkwan University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.6, 2021 , pp. 749-756 More about this Journal
Abstract
Thanks to PLC and IoT Sensor, huge amounts of data has been accumulated onto the companies' databases. Machine Learning Algorithms for the predictive model with good performance have been widely utilized in the manufacturing process. We present how to improve the performance of machine learning predictive models. To improve the performance of the predictive model, typical techniques such as increasing the sample size, optimizing the hyper parameters for the algorithm, and selecting a proper machine learning algorithm for the predictive model would be shown. We suggest some new ways to make the model performance much better. With the proposed methods, we can build a better predictive model for predicting and controlling product qualities and save incredibly large amount of quality failure cost.
Keywords
Machine learning; Predictive performance; Machine learning model; Model performance;
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