An Improved ML-kNN Algorithm by Fusing Nearest Neighbor Classification

Abstract

Based on the ML-kNN multi-label classification algorithm of probability and statistics, the k neighbors of unclassified sample are implicitly deemed that they have the same effect on classification result while ignoring the influence of the distances between k neighbors and unclassified sample. This paper proposes an improved ML-kNN algorithm by fusing nearest neighbor classification: IML-kNN. On the basis of the traditional ML-kNN algorithm, the algorithm considers the influence of the nearest neighbor and k neighbors of unclassified sample. Numerical simulation results show that the IML-kNN algorithm can have a good classification effect on the multi-label evaluation metrics.