An Improved KNN Algorithm Based on Variable Precision Rough Sets

K Nearest Neighbor(KNN) is a simple,stable and effective supervised classification algorithm in machine learning and is used in many practical applications.Its complexity increases with the number of instances,and thus it is not practicable for large-scale or high dimensional data.In this paper,an improved KNN algorithm based on variable parameter rough set model(RSKNN) is proposed.By introducing the concept of upper and lower approximations in variable precision rough set model,the instances of each class are classified into core and boundary areas,and the distribution of the training set is obtained.For a new instance,RSKNN firstly computes the area it belongs to.Then,according to the area information,the algorithm determines the category directly or searches k-nearest neighbors among the related areas instead of all areas.In this way,the computing cost is reduced and the robustness is enhanced.The experimental results for selected UCI datasets show that the proposed method is more effective than the traditional KNN with high classification accuracy.