treated as independent, selecting fifty features requires the same amount of processing as selecting two. By computing the correlations in parallel and sharing the response distance matrix across correlations, DC-SIS is able to rank all 755 predictors across all 252 folds in 18 minutes. It requires 28 hours for mRMR to rank only the top 50 predictors across the 252 folds. This is a 90-fold increase in speed with no significant difference in predictive power.

Performing the mRMR and DC-SIS feature selection on the SETAP, ULC, and ARR datasets demonstrated a comparable difference in runtime as observed with the Parkinson’s data. Running mRMR on SETAP ($74 \times 84$) required 101 seconds while DC-SIS only required 0.72 for a 141-fold speed-up. For ULC ($168 \times 147$), mRMR took 19 minutes and DC-SIS only 4.6 seconds for a 251-fold speed-up. The Arrythmia data ($452 \times 279$) needed 5 hours for mRMR to run but only 4 minutes for DC-SIS, which is a 77-fold speed-up. This shows that the 60-fold improvement on the Parkinson’s data is not an anomaly and that the improvement amount varies depending on the size and shape of the data.

![Comparison of accuracy on different feature selection methods []{data-label="fig:acc.png"}](acc.png){width="54.00000%" 