NDLI: Mining production data with neural network & CART

Content Provider	IEEE Xplore Digital Library
Author	Li, M. Feng, S. Sethi, I.K. Luciow, J. Wagner, K.
Copyright Year	2003
Description	Author affiliation: Intelligent Inf. Eng. Lab, Oakland Univ., Rochester, MI, USA (Li, M.; Feng, S.; Sethi, I.K.)
Abstract	We present the preliminary results of a data mining study of a production line involving hundreds of variables related to mechanical, chemical, electrical and magnetic processes involved in manufacturing coated glass. The study was performed using two nonlinear, nonparametric approaches, namely neural network and CART, to model the relationship between the qualities of the coating and machine readings. Furthermore, neural network sensitivity analysis and CART variable rankings were used to gain insight into the coating process. Our initial results show the promise of data mining techniques to improve the production.
Sponsorship	IEEE Comput. Soc. Tech. Committee on Computational Intelligence IEEE Comput. Soc. Tech. Committee on Pattern Analysis and Machine Intelligence
Starting Page	731
Ending Page	734
File Size	263960
Page Count	4
File Format	PDF
ISBN	0769519784
DOI	10.1109/ICDM.2003.1251019
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-11-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Neural networks Coatings Chemical processes Mechanical variables control Electric variables control Chemical products Glass manufacturing Machinery production industries Testing
Content Type	Text
Resource Type	Article

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