Improved multi-class discrimination by Common-Subset-of-Independent-Variables Partial-Least-Squares Discriminant...

In this study, a new PLS-DA modelling approach for multi-class discriminant analysis, called Common-Subset-of-Independent-Variables Partial-Least-Squares Discriminant Analysis is proposed and evaluated. Because in this method Partial-Least-Squares models for one component are used, it is denoted as CSIV-PLS1-DA. In this method for each class vector, individual PLS1 models with individual model complexities are developed, based on one common set of independent variables, obtained after variable selection by the Final Complexity Adapted Models method, using the absolute values of the PLS regression coefficients, denoted as FCAM-REG. CSIV-PLS1-DA combines a common variable set for all class vectors, which is a characteristic of PLS2-DA, with the individual model complexity for each class vector, which is a characteristic of PLS1-DA. These characteristics make CSIV-PLS1-DA more flexible than PLS2-DA. CSIV-PLS1-DA is found to be an alternative for PLS1-DA or PLS2-DA when the correlations between the responses are low, which is often the case in discriminant analysis. The performance of the CSIV-PLS1-DA method is investigated using one simulated and eight real multi-class data sets from different sources. The classification abilities, measured by the percentage classification accuracy rates (%Acc), resulting from CSIV-PLS1-DA, are statistically compared with those of PLS1-DA and PLS2-DA, using one-tailed paired t-tests at the 95% confidence level. The results show that the %Acc values resulting from the CSIV-PLS1-DA method are significantly higher than those of the corresponding PLS1-DA and PLS2-DA methods, meaning that the classification ability of the CSIV-PLS1-DA method is significantly better.