Heterogeneous Multi-Classifiers for Moving Vehicle Noise Classification using Bootstrap Method
Najihah Abdul Rahim, Murugesa Pandiyan Paulraj, Abdul Hamid Adom · 2013
In this paper, a simple system has been proposed to identify the type and distance of a moving vehicle using multi-classifier system (MCS). One-third octave filter bank approach has been used for extracting the significant feature from the noise emanated by the moving vehicle. The extracted features were associated with the type and distance of the moving vehicle and the heterogeneous multi-classifier system (HTMCS) based on multilayer Perceptron (MLP), K-nearest neighbor (KNN) and support vector machines (SVM) has been developed. Bootstrap sampling method based HTMCS was developed and the developed model has yielded a higher classification accuracy when compared to the individual base classifier models.