Reducing Support Vector Machine Classification Error by Implementing Kalman Filter
Muhsin Hassan, Dino Isa, Rajprasad Kumar Rajkumar, Nik Ahmad Akram, Roselina Arelhi · International Journal of Intelligent Systems and Applications · 2013
The aim of th is is to demonstrate the capability of Kalman Filter to reduce Support Vector Machine classification errors in classifying pipeline corrosion depth.In pipeline defect classification, it is important to increase the accuracy of the SVM classification so that one can avoid misclassification which can lead to greater problems in mon itoring pipeline defect and prediction of pipeline leakage.In this paper, it is found that noisy data can greatly affect the performance of SVM.Hence, Kalman Filter + SVM hybrid technique has been proposed as a solution to reduce SVM classification errors.The datasets has been added with Additive White Gaussian Noise in several stages to study the effect of noise on SVM classification accuracy.Three techniques have been studied in this experiment, namely SVM , hybrid of Discrete Wavelet Transform + SVM and hybrid of Kalman Filter + SVM.Experiment results have been compared to find the most promising techniques among them.MATLAB simu lations show Kalman Filter and Support Vector Machine co mbination in a single system produced higher accuracy compared to the other two techniques.