Oversampling based Classifiers for Categorization of Radar Returns from the Ionosphere
Surabhi Adhikari, Surendrabikram Thapa, Bickey Kumar Shah
Abstract
Study of the ionosphere is important for research in various domains. Especially in communication systems, this study holds a great importance. In ionospheric research, there is a need to delineate useful and non-useful radar returns from the ionosphere. The useful radar returns can be used for further analysis and non-useful radar returns can be discarded. When the usefulness of radar returns is analyzed by humans, it is simply time-consuming and is prone to more human errors. Thus, some machine learning methods are needed to delineate useful and non-useful radar returns. The various machine learning algorithms (classical learning algorithms and ensemble learners) as well as deep learning models are tested in this study. After 10-fold cross validation, the highest accuracy by Random Forest was 94.22% and the accuracy for a single layer neural network was 99.43%.