Adaptive Weighted Neighbors Method for Sensitivity Analysis

Identifying key factors from observational data is important for understanding complex phenomena in many disciplines, including biomedical sciences and biology. However, there are still some limitations in practical applications, such as severely nonlinear input-output relationships and highly skewed output distributions. To acquire more reliable sensitivity analysis (SA) results in these extreme cases, inspired by the weighted k-nearest neighbors algorithm, we propose a new method called adaptive weighted neighbors (AWN). AWN makes full use of the information contained in all training samples instead of limited samples and automatically gives more weight to nearby samples. Then, the bootstrap technique and Jansen's method are used to obtain reliable SA results based on AWN. We demonstrate the performance and accuracy of AWN by analyzing various biological and biomedical data sets, three simulated examples and two case studies, showing that it can effectively overcome the above limitations. We therefore expect it to be a complementary approach for SA.