Hierarchical Classification using Shrunken Centroids

There are various types of classifiers that can be trained on gene expression data with class labels. Many of them have an embedded mechanism for feature selection, by which they distinguish a subset of significant genes that are used for future prediction. When dealing with more than two class labels, especially when the number goes up to a dozen or more, people find it useful to know the relative affinity among the classes and different subsets of genes involved in discriminating different groups of classes. It provides them with more information not only when analyzing the relationship among classes, but also when predicting on future instances. We have achieved this by developing a hierarchical adaptation of the nearest shrunken centroid classifier. Here, we demonstrate our new method using a cancer data example.