An Improved k-Nearest Neighbor Classification Algorithm Using Shared Nearest Neighbor Similarity
نویسندگان
چکیده
k-Nearest Neighbor (KNN) is one of the most popular algorithms for pattern recognition. Many researchers have found that the KNN classifier may decrease the precision of classification because of the uneven density of t raining samples .In view of the defect, an improved k-nearest neighbor algorithm is presented using shared nearest neighbor similarity which can compute similarity between test samples with nearest neighbor samples. The experiment shows that this method can enhance classification precision compare to the traditional KNN.
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