Nearest Shrunken Centroid as Feature Selection of Microarray Data

The nearest shrunken centroid classifier uses shrunken centroids as prototypes for each class and test samples are classified to belong to the class whose shrunken centroid is nearest to it. In our study, the nearest shrunken centroid classifier was used simply to select important genes prior to classification. Random Forest, a decision tree based classification algorithm, is chosen as a classifier to seven cancer microarray data for correct diagnosis. Classification was also performed using the nearest shrunken centroid classifier and its results are compared to those from random Forest. Our study demonstrates that the nearest shrunken centroid classifier is simple, yet efficient in selecting important genes, but does not perform well as a classifier. We report that performance of Random Forest as a classifier is far superior to that of Shrunken centroid classifier.