Aiming at the problem that the Local Mean-based Pseudo Nearest Neighbor (LMPNN) algorithm is sensitive to the value of k and ignores the different influence of different attributes on the classification results, a Parameter Independent Weighted Local Mean-based Pseudo Nearest Neighbor classification (PIW-LMPNN) algorithm was proposed. Firstly, the Success-History based parameter Adaptation for Differential Evolution (SHADE) algorithm, the latest variant of differential evolution algorithm, was used to optimize the training set samples to obtain the best k value and a set of best weights related to the classes. Secondly, when calculating the distance between samples, different weights were assigned to different attributes of different classes, and the test set samples were classified. Finally, simulations were performed on 15 real datasets and the proposed algorithm was compared to other eight classification algorithms. The results show that the proposed algorithm has the classification accuracy and F1 value increased by about 28 percentage points and 23.1 percentage points respectively. At the same time, the comparision results of Wilcoxon signed-rank test, Friedman rank variance test and Hollander-Wolfe's pairwise processing show that the proposed improved algorithm outperforms the other eight classification algorithms in terms of classification accuracy and k value selection.