Algorithm 2 Ensemble Feature Selection.

1.Input: A dataset D with m samples and n features, and a positive integer K indicating the number of features to select. 2.Output: A subset of K features that are highly correlated with the target variable but uncorrelated with each other. 3.Extract graphical features using fast fractional Fourier transform. 4.For $i=1$ to n: (a) Calculate information gain (IG) for feature i using the dataset D and feature A: $$IG(D,A)=H\left(D\right)-H\left(D\right|A)$$ (b) Calculate ReliefF score for feature i based on differences between samples: $$ReliefF\left(i\right)=\sum _{j=1}^m\frac{-\mathrm{diff}(i,j)}{m}$$ (c) Calculate variance score for feature i: $$Variance\left(i\right)=\frac{1}{m}\sum _{j=1}^m{(X_{j,i}-{\overline{X}}_i)}^2$$ (d) Calculate NCA score for feature i based on the conditional probability $p\left(i\right|j)$: $$NCA\left(i\right)=\sum _{j=1}^{m}p\left(i\right|j)$$ (e) Calculate CFS score for feature i by considering correlations between features and the target variable: $$CFS\left(i\right)=\frac{\mathrm{cor}(X_i,Y)}{\sqrt{\mathrm{var}\left(X_i\right)·\mathrm{var}\left(Y\right)}}·\frac{2}{\mathrm{cor}(X_i,X_i)+\mathrm{cor}(Y,Y)}$$ 5.Combine scores for each feature by taking their average or using a weighted average. 6.Select the top-K features based on their scores.