Algorithm 1: Variance Threshold Feature Selection

1: Input: Dataset D with m features: f1, f2, …, fm. Variance threshold value τ. k: Desired number of features to select. 2: Output: A subset of features whose variance is above 3: Initialization: Create an empty list R to store the retained features 4: Feature Selection:   For each feature fi in (D). Compute the variance vi of fi    Add fi to the list R    end for 5: Return: Return the list R as the subset of features with variance above τ. 6: End