Title: The Effective coalitions of Shapley value For Integrated Gradients. Abstract: Many methods aim to explain deep neural networks (DNN) by attributing the prediction of DNN to its input features, like Integrated Gradients and Deep Shap, which both have critical baseline problems. Previous studies pursue a perfect but intractable baseline value, which is hard to find and has a very high computational cost, limiting the application range of these baseline methods. In this paper, we propose to find a set of baseline values corresponding to Shapley values which are easier to be found and have a lower computation cost. To solve computation dilemma of Shapley value, we propose Effective Shapley value (ES), a proportional sampling method to well simulate the ratios between the Shapley values of features and then propose Shapley Integrated Gradients (SIG) to combine Integrated Gradients with ES, to achieve a good balance between efficiency and effectiveness. Experiment results show that our ES method can well and stably approximate the ratios between Shapley values, and our SIG method has a much better and more accurate performance than common baseline values with similar computational costs.