The Influence of Multi-class Feature Selection on the Prediction of Diagnostic Phenotypes

摘要

In this work, we evaluate two schemes for incorporating feature selection processes in multi-class classifier systems on high-dimensional data of low cardinality. These schemes operate on the level of the systems’ individual base classifiers and therefore do not perfectly fit in the traditional categories of filter, wrapper and embedded feature selection strategies. They can be seen as two examples of feature selection networks that are only loosely related to the structure of the multi-class classifier system. The architectures are tested for their application in predicting diagnostic phenotypes from gene expression profiles. Their selection stability and the overall generalization ability are evaluated in \(10 \times 10\) cross-validation experiments with support vector machines, random forests and nearest neighbor classifiers on eight publicly available multi-class microarray datasets. Overall the feature selecting multi-class classifier systems were able to outperform their counterparts on at least five of eight datasets.