Support Vector Machine Integrated CCA for Classification of Complex Chemical Patterns

SVM for classification is sensitive to noise and multicollinearity between attributes. Correlative component analysis (CCA) was used to eliminated multicollinearity and noise of original sample data before classified by SVM. To improve the SVM performance, Eugenic Genetic Algorithm (EGA) was used to optimize the parameters of SVM. Finally, a typical example of two classes natural spearmint essence was employed to verify the effectiveness of the new approach CCA-EGA-SVM. The accuracy is much better than that obtained by SVM alone or self-organizing map (SOM) Integrated with CCA.