The Relief algorithm is a filtering feature selection algorithm that maximizes the instance margins in the nearest neighbor classifier in a greedy manner. Combined with the local weight method, the authors proposed a Class Dependent RELIEF (CDRELIEF) algorithm that trains one feature weight for each category. This method can better reflect the correlation of features. However, feature weight vector are only effective for measuring the correlation of features to a certain class, and classifying them in actual classification. In the actual classification, the classification accuracy is not high enough. In order to apply the CDRELIEF algorithm to the classification process, this study changes the weight update process, and assigns an instance weight to each instance in the training set. By combining the instance weight value into the weight updating formula, the influence of data points far from the classification boundary and outliers on weight updating is excluded, thereby improving the classification accuracy. The Instance Weighted CDRELIEF (IWCDRELIEF) algorithm proposed in this study is compared with CDRELIEF algorithm on multiple UCI 2-class datasets. Experimental results show that the algorithm proposed in this study has significantly improved the CDRELIEF algorithm.