Improved PMI-based input variable selection approach for artificial neural network and other data driven environmental and water resource models
We address the performance of the PMI IVS influenced by the normality of data. We improved the performance of the PMI IVS for non-normally distributed data. Conventional PMI IVS performs well only for data following Gaussian distribution. Bandwidth with reduced Gaussian assumption improves the accuracy of PMI IVS. Preliminary guidelines are developed for PMI IVS and successfully validated.
input variable selection artificial neural networks general regression neural networks partial mutual information kernel bandwidth kernel density estimation environment hydrology and water resources