Dr. Hafiz Imtiaz

In this work, we present a comparative analysis of five different machine learning algorithms, namely Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Random Forest (RF), Naive Bayes (NB), and anomaly detection, in classifying different types of single-stage and multiple-stage PQDs. We generated a dataset consisting of 29 types of PQDs, while taking into consideration the variation of fundamental frequency. We note that variation of fundamental frequency can significantly affect the classification accuracy of the machine learning models. We introduced different levels of noise to the PQD data to mimic real-life scenarios. We used the Linear Discriminant Analysis (LDA) for dimensionality reduction and feature extraction. Our results show that the classification accuracy decreases as the noise level is increased and that a decrease in the classification accuracy is possible when the variation of the fundamental frequency is considered. This work led to this Measurement publication.