Defect rate evaluation via simple active learning

Defect rate evaluation via simple active learning In the preparatory stage of product manufacturing, its defect risk is often evaluated by checking whether experimentally manufactured products cause the defect or not. The experimentally manufacturing is conducted for various values of variables which may related the defect, but manufacturing products for all combinations of the values will cost a lot especially when the number of variables is large. To overcome this problem, active learning methods which may be able to evaluate the defect risk efficiently by selecting values purposefully are considered. In this paper, it is pointed out that even a modern active learning method is inappropriate if the nonlinearity of the relation between the variables and the defect is strong and if the defect rate is small. And then a simple active learning method which can work well for such a case is proposed. Through simulation studies and real data analysis, the validity of the proposed method is checked. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Pacific Journal of Mathematics for Industry Springer Journals
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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2015 by Umezu et al.
Subject
Mathematics; Applications of Mathematics; Quantitative Finance; Mathematical Applications in Computer Science; Mathematical Applications in the Physical Sciences; Mathematical Modeling and Industrial Mathematics; Math Applications in Computer Science
eISSN
2198-4115
D.O.I.
10.1186/s40736-015-0019-z
Publisher site
See Article on Publisher Site

Abstract

In the preparatory stage of product manufacturing, its defect risk is often evaluated by checking whether experimentally manufactured products cause the defect or not. The experimentally manufacturing is conducted for various values of variables which may related the defect, but manufacturing products for all combinations of the values will cost a lot especially when the number of variables is large. To overcome this problem, active learning methods which may be able to evaluate the defect risk efficiently by selecting values purposefully are considered. In this paper, it is pointed out that even a modern active learning method is inappropriate if the nonlinearity of the relation between the variables and the defect is strong and if the defect rate is small. And then a simple active learning method which can work well for such a case is proposed. Through simulation studies and real data analysis, the validity of the proposed method is checked.

Journal

Pacific Journal of Mathematics for IndustrySpringer Journals

Published: Oct 20, 2015

References

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