Features are ranked based upon their relevance with class labels and redundancy compared to other features.

In addition to the mutual information method employed by mRMR, distance correlation can also be considered for measuring the relevance of a feature. Distance correlation can use Euclidean distance, Manhattan distance, Minkowski distance, cosine similarity, or any other metric to measure the dependence between features, and to identify which are relevant. We can also greatly simplify the process by removing the minimum-redundancy constraint from mRMR and focus solely on maximum relevance. Li et al. call this method Distance Correlation Sure Independent Selection (DC-SIS) [@dcsis]. Once a set of maximally relevant features are selected, machine learning classifiers such as Random Forest, Support Vector Classifier (SVC), k-Nearest-Neighbor(k-NN), Multilayer perceptron, Logistic regression, Naive Bayes are applied on the new datasets.

The main reason behind choosing the DC-SIS over mRMR is that mRMR requires too much processing time to perform feature selection on the PD dataset in [@compare] even when running on C code in parallel.

Performing the mRMR feature selection in a jackknife estimate of performance required 28 hours on 2.2 GHz Intel Core i7 processor using the `pymrmr` Python package. Performing a comparable selection using DC-SIS 