An Ensemble Feature Selection Framework Integrating Stability Ensemble feature selection has drawn more and more attention in recent years. There are mainly two strategies for ensemble feature selection, namely data perturbation and function perturbation. Data perturbation performs feature selection on data subsets sampled from the original dataset and then selects the features consistently ranked highly across those data subsets. Function perturbation frees the user from having to decide on the most appropriate selector for any given situation and works by aggregating multiple selectors. Our study showed that function perturbation resulted in a low stability. We therefore propose a framework, Ensemble Feature Selection Integrating Stability (EFSIS), combining these two strategies and integrating stability during the aggregation of selectors. Empirical results indicate that EFSIS highly improves stability and meanwhile, maintains the prediction accuracy.

Optimal Feature Aggregation and Combination for TwoDimensional Ensemble Feature Selection Feature selection is a way of reducing the features of data such that, when the classification algorithm runs, it produces better accuracy. In general, conventional feature selection is quite unstable when faced with changing data characteristics. It would be inefficient to implement individual feature selection in some cases. Ensemble feature selection exists to overcome this problem. However, with the advantages of ensemble feature selection, some issues like stability, threshold, and feature aggregation still need to be overcome. We propose a new framework to deal with stability and feature aggregation. We also used an automatic threshold to see whether it was efficient or not; the results showed that the proposed method always produces the best performance in both accuracy and feature reduction. The accuracy comparison between the proposed method and other methods was 0.5xe2x80x9314% and reduced more features than other methods by 50%. The stability of the proposed method was also excellent, with an average of 0.9. However, when we applied the automatic threshold, there was no beneficial improvement compared to without an automatic threshold. Overall, the proposed method presented excellent performance compared to previous work and standard ReliefF.