Mean Aggregation versus Robust Rank Aggregation for Ensemble Gene Selection

Feature (gene) selection is an important preprocessing step for performing data mining on large-scale bioinformatics datasets. However, one known concern is that feature selection can sometimes give very different results when applied to very similar data sets. Ensemble gene selection is a promising new approach which may help resolve this concern, producing more stable gene lists and better classification results. Ensemble selection consists of multiple runs of feature ranking which are then combined into a single ranking for each feature. However, one of the most critical decisions when performing ensemble gene selection is deciding on which aggregation technique to use for combining the resulting ranked feature lists from the multiple runs of feature ranking into a single decision for each gene. This paper is an in-depth comparison between two aggregation techniques: Mean Aggregation (a simple and commonly-used technique) and Robust Rank Aggregation (a recently proposed aggregation technique designed specifically for bioinformatics). Our results show that in general Mean Aggregation will outperform (or at least match) Robust Rank Aggregation in terms of classification performance, while being significantly simpler to implement and perform. These results allows us to recommend with reasonable confidence the use of Mean Aggregation over Robust Rank Aggregation.