Solving the Problem of the K Parameter in the KNN Classifier Using an Ensemble Learning Approach
This addresses a specific parameter-tuning issue in machine learning classification, but it is incremental as it builds on existing ensemble methods.
The paper tackles the problem of selecting the K parameter in the KNN classifier by proposing an ensemble learning approach that combines weak classifiers with different K values, showing it outperforms traditional KNN and is competitive with other classifiers.
This paper presents a new solution for choosing the K parameter in the k-nearest neighbor (KNN) algorithm, the solution depending on the idea of ensemble learning, in which a weak KNN classifier is used each time with a different K, starting from one to the square root of the size of the training set. The results of the weak classifiers are combined using the weighted sum rule. The proposed solution was tested and compared to other solutions using a group of experiments in real life problems. The experimental results show that the proposed classifier outperforms the traditional KNN classifier that uses a different number of neighbors, is competitive with other classifiers, and is a promising classifier with strong potential for a wide range of applications.