Classification Algorithm Based on Natural Nearest Neighbor

K-nearest Neighbor (KNN) is a widely used classiflcation method that operates by a majority vote on a k{ nearest neighbor set. But when the KNN is used to deal with the datasets with difierent characteristics in classiflcation, it is di‐cult to select an appropriate parameter k which afiects obviously the performance and e‐ciency of the algorithm. Therefore, how to select appropriate parameter k value of KNN algorithm has been an open issue in the fleld of data mining. So, Natural Nearest Neighbor (3N) proposed by us is a novel concept on nearest neighbor, which does not need a parameter K and in which the neighbors of each point are obtained by an adaptive algorithm. This paper proposes a Classiflcation Algorithm based on Natural Nearest Neighbor (CAb3N). Comprehensive experimental results on the UCI dataset conflrm the claims that CAb3N not only has the advantage of parameter-free, but also has the better accuracy and overall performance both than the traditional KNN algorithm and hubness-based weighted KNN classiflcation algorithm.