Improving the Precision of KNN Classifier using Nonlinear Weighting Method Based on the Spline Interpolation

Precision improvement of the classifiers is one of the main challenges for the Artificial Intelligence researchers. Feature weighting is one of the most common ideas in this area. In this study, in order to increase the accuracy of the K-Nearest Neighbors (KNN) classifier, a nonlinear feature weighting method based on the Spline interpolation is used. In this approach, a unique nonlinear function is estimated for each feature. In order to find the best estimated parameters of the nonlinear function which is suitable for each feature, the evolutionary Genetic Algorithm is applied. Numerical results show that the nonlinear weighting method increases the accuracy of the classifiers compared to the linear weighting method.