An Improved KNN Algorithm Based on Multi-attribute Classification
Zhang Jong-hu · Journal of Anshan Normal University · 2013
To improve the classification accuracy of the conventional Euclidean KNN algorithm and the improved KNN algorithm based on information entropy,this paper proposes an improved KNN algorithm based on multi-attribute classification. The procedures of the new algorithm comprise: i) classify the attributes according to the percentage of their attribute values in an entire attribute of sample set into those discrete attributes suitable for entropy-based KNN algorithm and those continuous attributes suitable for conventional Euclidean KNN similarity-based algorithm; ii) process the two types of attributes separately and then sum up the two series of results with weighing and put the sum as the distance between samples; iii) select k samples those are closest to the test sample to determine the decision attribute type of the test sample.