Hybrid Rank Aggregation (HRA): A novel rank aggregation method for ensemble-based feature selection

Abstract

Background: Feature selection (FS) reduces the dimensions of high dimensional data. Among many FS approaches, ensemble-based feature selection (EFS) is one of the commonly used approaches. The rank aggregation (RA) step influences the feature selection of EFS. Currently, the EFS approach relies on using a single RA algorithm to pool feature performance and select features. However, a single RA algorithm may not always give optimal performance across all datasets. Method and Results: This study proposes a novel hybrid rank aggregation (HRA) method to perform the RA step in EFS which allows the selection of features based on their importance across different RA techniques. The approach allows creation of a RA matrix which contains feature performance or importance in each RA technique followed by an unsupervised learning-based selection of features based on their performance/importance in RA matrix. The algorithm is tested under different simulation scenarios for continuous outcomes and several real data studies for continuous, binary and time to event outcomes and compared with existing RA methods. The study found that HRA provided a better or at par robust performance as compared to existing RA methods in terms of feature selection and predictive performance of the model. Conclusion: HRA is an improvement to current single RA based EFS approaches with better and robust performance. The consistent performance in continuous, categorical and time to event outcomes suggest the wide applicability of this method. While the current study limits the testing of HRA on cross-sectional data with input features of a continuous distribution, it could be applied to longitudinal and categorical data.