 correlation between features. This may lead to lose much useful information to learn the local explanation models. Moreover, most existing methods assume the decision boundary is local linearity, which may produce serious errors as in most complex networks, the local decision boundary is non-linear.  In this paper, we design and develop a novel, high-fidelity local explanation method to address the above challenges. First, we design a unique local sampling process which incorporate the feature clustering method to handle the feature dependency problems. Then, we adopt S