A Novel Ensemble Feature Selection Technique for Cancer Classification Using Logarithmic Rank Aggregation Method

Abstract

Recent studies have shown that ensemble feature selection (EFS) has achieved outstanding performance in microarray data classification. However, some issues remain partially resolved, such as suboptimal aggregation methods and non-optimised underlying FS techniques. This study proposed the logarithmic rank aggregate (LRA) method to improve feature aggregation in EFS. Additionally, a hybrid aggregation framework was presented to improve the performance of the proposed method by combining it with several methods. Furthermore, the proposed method was applied to the feature rank lists obtained from the optimised FS technique to investigate the impact of FS technique optimisation. The experimental setup was performed on five binary microarray datasets. The experimental results showed that LRA provides a comparable classification performance to mean rank aggregation (MRA) and outperforms MRA in terms of gene selection stability. In addition, hybrid techniques provided the same or better classification accuracy as MRA and significantly improved stability. Moreover, some proposed configurations had better accuracy, sensitivity, and specificity performance than MRA. Furthermore, the optimised LRA drastically improved the FS stability compared to the unoptimised LRA and MRA. Finally, When the results were compared with other studies, it was shown that optimised LRA provided a remarkable stability performance, which can help domain experts diagnose cancer diseases with a relatively smaller subset of genes.