Consensus Group Stable Feature Selection


Stability is an important issue in feature selection from high-dimensional and small sample data. CGS is a feature selection algorithm developed under a novel framework for stable feature selection which first identifies consensus feature groups from subsampling of training samples, and then performs feature selection by treating each consensus feature group as a single entity. Experiments on both synthetic and real-world data sets show that CGS algorithm is effective at alleviating the problem of small sample size and leads to more stable feature selection results and comparable or better generalization performance than state-of-the-art feature selection algorithms.

CGS Software

This software package is prepared in Java. It is provided free of charge to the research community as an academic software package with no commitment in terms of support or maintenance.