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Proceedings of the 2010 International Conference on Mechanical, Industrial, and Manufacturing Technologies (MIMT 2010)
By
International Association of Computer Science and Information Technology (IACSIT)
International Association of Computer Science and Information Technology (IACSIT)
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ISBN:
9780791859544
No. of Pages:
590
Publisher:
ASME Press
Publication date:
2010
eBook Chapter
48 Improved Partial Least Squares Regression with Rough Set and Its Applications in the Modeling of LCC
By
Xiao-Hai Zhang
,
Xiao-Hai Zhang
College of Ships and Power,
Naval University of Engineering
, Wuhan
, China
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Jia-Shan Jin
,
Jia-Shan Jin
College of Ships and Power,
Naval University of Engineering
, Wuhan
, China
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Jun-Bao Geng
,
Jun-Bao Geng
College of Ships and Power,
Naval University of Engineering
, Wuhan
, China
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Jun-Tao Zhang
,
Jun-Tao Zhang
College of Ships and Power,
Naval University of Engineering
, Wuhan
, China
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Linkai Sun
Linkai Sun
College of Ships and Power,
Naval University of Engineering
, Wuhan
, China
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Page Count:
7
-
Published:2010
Citation
Zhang, X, Jin, J, Geng, J, Zhang, J, & Sun, L. "Improved Partial Least Squares Regression with Rough Set and Its Applications in the Modeling of LCC." Proceedings of the 2010 International Conference on Mechanical, Industrial, and Manufacturing Technologies (MIMT 2010). Ed. International Association of Computer Science and Information Technology (IACSIT). ASME Press, 2010.
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Establish the precise cost model is one of the most important content of Life Cycle Cost (LCC). In order to find out the important factors in the modeling of LCC, and reduce the correlativity between the factors that affect the cost model, the method of improved Partial Least Squares (PLS) regression with Rough Set was proposed to enhance the calculation precision of the LCC modeling. The attributes of the data were reduced, redundant information were deleted, and then to PLS regression. The deficiency of the PLS regression were improved by this method. Compared with the method of PLS regression, the precision of improved PLS regression model with Rough Set was higher and its application was quite comprehensive.
Abstract
Key Words
1. Introduction
2. Rough Set Theory
3. Improved PLS Regression with Rough Set
4. Applications in Ships LCC
5. Conclusion
References
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