Knn Svm Comparison. The pulmonary acoustic signals used in this study were obtained from the RALE lung sound database. The KNN classifier compares this histogram to those already generated from the training images.
The classifiers ADABOOST KNN SVM-RBF and logistic regression were applied to the original random oversampling and undersampling data sets. When classifying the kNN will generally classify accurately. Whereas if the model testing is done SVM algorithm is the best accuracy model compared to Decisioan Tree and KNN.
The accuracy results are relatively the same as music classification based on audio feature extraction so the classification with the extraction of metadata features can continue to be developed if the metadata in.
The proposed system is evaluated using side view videos of NLPR database. Results show that ADABOOST KNN and SVM-RBF exhibits over-fitting when applied to the original dataset. ANNs have been observed to be limited by insufficient training data also. However in model prediction the accuracy of LR model is higher than that of other five non-parametric models under various ETTC thresholds conditions.