power.

              **mRMR**   **DC-SIS**   **Between**
  ----------- ---------- ------------ -------------
  **PD**      0.96       0.98         0.42
  **ARR**     0.97       0.99         0.43
  **ULC**     0.93       0.98         0.45
  **SETAP**   0.95       0.94         0.55

  : Percentage of features shared within and between selection methods[]{data-label="tab:feat"}

Discussion
==========

Using DC-SIS is not meant as a global replacement for mRMR and other entropy-based feature selectors. We present it as a significantly more economical option when initially exploring the data. Since it can rank all of the features around 100 times faster than mRMR and can rank the top 50, DC-SIS can be used to find a reasonable upper bound for other, more expensive, feature selection methods. There is no sense in using mRMR to find the top 50 features in 28 hours, when you can show that only 23 are required using DC-SIS.

As future work, we can compare this performance with several other preprocessing techniques such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA). We can also investigate whether the predictive performance of DC-SIS generalizes to other Machine Learning Methods with datasets of different shapes. It is also possible to develop a distance correlation selection algorithm which takes into account feature redundancy. It could use stepwise selection like mRMR or 