For the single or synthetic features selection problems, based on the integrated square error(ISE) criterion and random permutation, a supervised feature ranking criterion of a single feature is proposed firstly. Then, such a random permutation is extended to synergetic features, and accordingly the synergetic feature selection method is developed. Finally, the optimal feature subset is determined by the classification accuracy obtained by the kernel neural network(KNN) method. Experimental results on synthetic and real datasets show the effectiveness of the proposed algorithm.