Low redundancy feature selection with grouped variables and its application to healthcare data

In the current era massive datasets in healthcare are becoming much more available for analysis, where numerous features are designed or constructed to represent a patient. Feature selection algorithms play a key role in reducing the data dimension thus speeding up the succeeding learning algorithms as well as improving predicting accuracy. How to select the appropriate subset of features with low redundancy is one of the interesting problem in feature selection. In this paper, we present a new feature selection algorithm which aims to select low redundant features in the setting of grouped variables. We adopt a global optimization method based on Lipschitz continuity and present evaluation results on several datasets, which demonstrates the correctness and effectiveness of our algorithm.