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COMPARATIVE ANALYSIS OF DIMENSION REDUCTION TECHNIQUES FOR CLASSIFICATION OF RADAR RETURNS FROM IONOSPHERE USING A MODEL BASED ON MACHINE LEARNING TECHNIQUE

Advances in technology have immensely increased data collection and storage capabilities during the past decades. This progression has overloaded many science fields with huge amount of raw data. Scientists and researchers working in domains as diverse as engineering, medicine, economics etc are facing problems handling and performing analysis on the large database gathered from different resources. This data overload is presenting new challenges in data analysis. Traditional statistical methods fail in such scenarios because, as the data dimensionality increases the number of variables also increase which in turn increases the complexity of the system. The processing of high-dimensional datasets is computationally expensive. To cope up with this problem, Dimension Reduction Techniques are applied. Some of the applications in which dimension reduction techniques are incorporated are pattern recognition, image & video processing, signal processing, classification & regression problems, defense applications, bioinformatics etc. In Pattern Recognition, Object Recognition and classification problems, dimension reduction techniques are used as a pre-process. The reduced dataset is then applied to a classifier for recognition. In this research paper, a comparative study is conducted of two dimension reduction techniques for the binary classification problem in order to analyze the efficiency of HF (High Frequency) Wireless Communication in a given environment, using a benchmarked Ionosphere data set from the UCI Machine Learning repository. The two dimension reduction techniques compared are PCA (Principal Component Analysis) and ICA (Independent Component Analysis). Performance based analysis is performed on the results generated after the application of ICA and PCA by implementing both the outcomes on a model based on Machine Learning Technique.