The stability of different aggregation techniques in ensemble feature selection

Abstract

Abstract To mitigate the curse of dimensionality in high-dimensional datasets, feature selection has become a crucial step in most data mining applications. However, no feature selection method consistently delivers the best performance across different domains. For this reason and in order to improve the stability of the feature selection process, ensemble feature selection frameworks have become increasingly popular. While many have examined the construction of ensemble techniques under various considerations, little work has been done to shed light on the influence of the aggregation process on the stability of the ensemble feature selection. In contribution to this field, this work aims to explore the impact of some selected aggregation strategies on the ensemble’s stability and accuracy. Using twelve classification real datasets from various domains, the stability and accuracy of five different aggregation techniques were examined under four standard filter feature selection methods. The experimental analysis revealed significant differences in both the stability and accuracy behavior of the ensemble under different aggregations, especially between score-based and rank-based aggregation strategies. Moreover, it was observed that the simpler score-based strategies based on the Arithmetic Mean or L2-norm aggregation appear to be efficient and compelling in most cases. Given the data structure or associated application domain, this work’s findings can guide the construction of feature selection ensembles using the most efficient and suitable aggregation rules.

Keywords

• Ensemble learning
• Feature selection
• Mean aggregation
• Stability