A KNN classifier with PSO feature weight learning ensemble

Feature selection and weighting are normally ways to improve KNN classification algorithm. In this paper, we use the reverse cloud algorithm to map the training samples into clouds. Each attribute is mapped to a cloud vector. Reverse cloud algorithm is not sensitive to the noise on data sets and it can eliminate the impact of noise on classification effectively. By comparing the similarity of clouds in the cloud vector, we can find out a fitness function to measure the feature weighting results. The weighting process is a typical optimizing problem. We present a KNN algorithm based on PSO feature weight learning and compare our approach with classic KNN algorithms and other well-known improved KNN algorithms on 10 data sets. Experiments show that our approach could achieve a better or at least a comparable classification accuracy with other algorithms.