In this work, we proposed a weighted discrete k-nearest neighborhood(WD-KNN) classification algorithm and compared it with several weighting schemes on KNN-based classifiers, including traditional K-Nearest Neighborhood (KNN), weighted KNN (WKNN), KNN classification using Categorical Average Patterns (WCAP), and WD-KNN. The highest recognition rate is 81.4% with WD-KNN classifier weighted by Fibonacci sequence. Then we evaluated the performance of several classifiers, including KNN, MKNN, WKNN, LDA, QDA, GMM, HMM, SVM, BPNN, and the proposed WD-KNN, for detecting emotion from Mandarin speech....
In this work, we proposed a weighted discrete k-nearest neighborhood(WD-KNN) classification algorithm and compared it with several weighting schemes o...
