The K-NN Classification Based On Evidence Reasoning Model

This dissertation mainly presents two new classification methods:the k-NN classification method based on a evidence reasoning model and the k-NN classification method based on variable precision rough set model.During the former classification method,the classification expert gives the weights of the nearest neighbor sample points of the sample point to be classified,then defines the key sample point and non-key sample point,furthermore gives their support degree,discount coefficient. Through the allusion of the former notions,we can ameliorate the k-NN classification method based on evidence theory,and make the classification result more precise.When the discount coefficient is 1 and all weights of the nearest neighbor sample points are the same,the k-NN classification method based on evidence reasoning model will become the k-NN classification method based on evidence theory. Furthermore,a example is given and a computer simulation is performed,then a good result is got.The latter classification method combined variable precision rough set model with k-NN classification method,so we can control the classification accuracy rate by the most endurable given error classification rate,then we can make the classification result conform to what we expect,and also some examples are given.