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This paper presents a new solution for choosing the K parameter in thek-nearest neighbor (KNN) algorithm, the solution depending on the idea ofensemble learning, in which a weak KNN classifier is used each time with adifferent K, starting from one to the square root of the size of the trainingset. The results of the weak classifiers are combined using the weighted sumrule. The proposed solution was tested and compared to other solutions using agroup of experiments in real life problems. The experimental results show thatthe proposed classifier outperforms the traditional KNN classifier that uses adifferent number of neighbors, is competitive with other classifiers, and is apromising classifier with strong potential for a wide range of applications.

Solving the Problem of the K Parameter in the KNN Classifier Using an Ensemble Learning Approach