required only 18 minutes, and produced predictions with performance not statistically distinguishable from mRMR.

The rest of the paper is presented as follows. In Section II, we shall describe the dataset that we used in our study and the related works. In Section III, we discuss our methodology and approach of feature selection. We present our results in Section IV. In Section V, we discuss the result, and implication of our study and potential future works. We conclude our study in Section VI.

Background
==========

Dataset Description
-------------------

The data for this experiment is the Parkinson’s Disease Classification Data Set [@compare] from the UCI Machine Learning Repository that consists of 756 observations of 755 variables. The observations are from 188 patients with PD, 107 male and 81 female, and their ages vary from 33 to 87. The control group contains 64 healthy individuals without PD, 23 male and 41 female, with ages between 41 and 82 [@compare]. Each subject contributes three observations to the dataset. Each observation is derived from the subject speaking, and holding, a vowel sound into a microphone. Gender is the only categorical predictor with the rest being quantitative coming from six large categories.

### Baseline features

