Pruning variable selection ensembles
This work addresses the need for more efficient and accurate variable selection methods in statistical learning, though it appears incremental as it builds on existing ensemble techniques like stability selection.
The paper tackles the problem of improving variable selection accuracy and reducing false discovery rates in ensemble learning by proposing a pruning strategy that creates smaller, more accurate ensembles through a greedy sorting and early stopping approach. Experimental results show that pruned StabSel achieves higher selection accuracy and lower false discovery rates compared to StabSel and other benchmarks.
In the context of variable selection, ensemble learning has gained increasing interest due to its great potential to improve selection accuracy and to reduce false discovery rate. A novel ordering-based selective ensemble learning strategy is designed in this paper to obtain smaller but more accurate ensembles. In particular, a greedy sorting strategy is proposed to rearrange the order by which the members are included into the integration process. Through stopping the fusion process early, a smaller subensemble with higher selection accuracy can be obtained. More importantly, the sequential inclusion criterion reveals the fundamental strength-diversity trade-off among ensemble members. By taking stability selection (abbreviated as StabSel) as an example, some experiments are conducted with both simulated and real-world data to examine the performance of the novel algorithm. Experimental results demonstrate that pruned StabSel generally achieves higher selection accuracy and lower false discovery rates than StabSel and several other benchmark methods.