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A study on warning/detection degree of warranty claims data using natural network learning
conference contribution
posted on 2023-05-23, 13:05 authored by Lee, SH, Seo, SC, Soonja YeomSoonja Yeom, Moon, K, Kang, MS, Kim, BGWarranty service is getting important since it is an agreement between manufacturers and consumers. An issue is to find out a lower level of agreement from the perspective of manufacturers and consumers. Thus, it is very important to determine early warning/detection degree of defected parts through warranty claims data. However, there are qualitative factors more than quantitative ones in the determination. The study thus provides a part-significance knowledge extraction method based on analytic hierarchy process analysis which is appropriate to analyze those qualitative factors as well as a process to extract a list of defected parts using neural network learning.
History
Publication title
Proceedings from the Sixth International Conference on Advanced Language Processing and Web Information TechnologyPagination
492-497ISBN
9780769529301Department/School
School of Information and Communication TechnologyPublisher
IEEE Computer SocietyPlace of publication
United StatesEvent title
Sixth International Conference on Advanced Language Processing and Web Information TechnologyEvent Venue
Henan, ChinaDate of Event (Start Date)
2007-08-22Date of Event (End Date)
2007-08-24Rights statement
Copyright 2007 IEEERepository Status
- Restricted