Improved Principal Components Regression with Rough Set and its Application in the Modeling of Warship LCC

There are many factors affect the warship life cycle cost (LCC), the importance of every factor is different, and the relationships between factors are correlated. In order to establish the precise LCC model, the principal components regression (PCR) and partial least squares regression (PLSR) are p...

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Bibliographic Details
Main Authors: Xiao-Hai Zhang, Jia-Shan Jin, Jun-Bao Geng
Format: Conference Proceeding
Language:English
Subjects:
Costs
Educational institutions
Filtering
Information analysis
Least squares methods
Life Cycle Cost(LCC)
Machine vision
Manufacturing
Marine vehicles
Partial Least Squares Resgression (PLSR)
Power engineering and energy
Principal Components Regression(PCR)
Rough Set
Set theory
warship
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Summary:There are many factors affect the warship life cycle cost (LCC), the importance of every factor is different, and the relationships between factors are correlated. In order to establish the precise LCC model, the principal components regression (PCR) and partial least squares regression (PLSR) are proposed to reduce the correlativity between factors which affect the modeling of LCC. However, the components often don't strongly explain the dependent variables when filtering principal components in the independent variables. Therefore, the improved PCR with rough set is proposed to overcome the correlativity between the variables, which could choose the important parameters and reduce the unimportant parameters in the modeling of LCC. The modeling of the process and the regression model are described in the content. Compared with the method of PCR and PLSR, the precision of the improved PCR with rough set is much higher.
DOI:10.1109/ICMV.2009.25