Feature weighting with Laplacian score

Speech processing is working area where the speech signal is digitized and processed. In this paper, it is used LSVT (Lee Silverman Voice Treatment) dataset that belonging to the people living speech disorder because of the Parkinson disease. The dataset contains a large number of features. A large number of features can be occurred negative effect on classification because of noise or less important features. Especially, it is suggested that k nearest neighbor is weighted to reduce this effect distance based classifiers. Therefore, it is benefited from Laplacian score to weight features. Consequently, classification accuracy is increased from 73.61% to 85.83% for k nearest neighbor classifier.