Maximum Separation Partial Least Squares (MSPLS): a new method for classification in microarray experiment

The purpose of the paper is to propose a new method for classification. Our MSPLS method was deduced from the classic Partial Least Squares (PLS) algorithm. In this method we applied the Maximum Separation Criterion. On the basis of the approach we are able to find such weight vectors that the dispersion between the classes is maximal and the dispersion within the classes is minimal. In order to compare the performance of classifier we used the following types of dataset - biological and simulated. Error rates and confidence intervals were estimated by the jackknife method.