Multivariate data analysis (4th ed.): with readings
Multivariate data analysis (4th ed.): with readings
The nature of statistical learning theory
The nature of statistical learning theory
Using neural networks for data mining
Future Generation Computer Systems - Special double issue on data mining
Predicting object-oriented software maintainability using multivariate adaptive regression splines
Journal of Systems and Software
A new two-stage hybrid approach of credit risk in banking industry
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Support vector machine approach for longitudinal dispersion coefficients in natural streams
Applied Soft Computing
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
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Effective identification of the change point of a multivariate process is an important research issue since it is associated with the determination of assignable causes which may seriously affect the underlying process. Most existing studies either use the maximum likelihood estimator (MLE) method or the machine learning (ML) method to estimate or identify the change point of a process. Typically, the MLE method may be criticized for its assumption that the process distribution is known, and the ML method may have the deficiency of using a large number of input variables in the modeling procedure. Diverging from existing approaches, this study proposes an integrated hybrid scheme to mitigate the difficulties of the MLE and ML methods. The proposed scheme includes four components: the logistic regression (LR) model, the multivariate adaptive regression splines (MARS) model, the support vector machine (SVM) classifier and the change point identification strategy. It performs three tasks in order to effectively identify the change point in a multivariate process. The initial task is to use the LR and MARS models to reduce and refine the whole set of input or explanatory variables. The remaining variables are then served as input variables to the SVM in the second task. The last task is to integrate use of the SVM outputs with our proposed identification strategy to determine the change point in a multivariate process. Experimental simulation results reveal that the proposed hybrid scheme is able to effectively identify the change point and outperform the typical statistical process control (SPC) chart alone and the single stage SVM methods.