期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2014
卷号：5
期号：2
页码：2361-2364
出版社：TechScience Publications
摘要：Learning is acquiring knowledge which makes man to study new things. The familiarity of the new concept is attained by the machine by giving repeated training on the same concept. In this way machines can also learn by repeated training on the same set of data. Data mining includes the concept of classification, which can be done by machine learning algorithms. Data that are to be classified can also be complex values. The performance varies if both the real and imaginary part are considered for classification. There are different machine algorithms with different features. Some of the machine learning algorithms such as Support Vector Machines (SVM), Extreme Learning Machines (ELM), Self- Adaptive Resource Allocation Network (SRAN) and Phase Encoded Complex-Valued Extreme Learning Machine (PECELM )are considered. This paper gives a comparison of these algorithms with its working nature and discusses the simulated results performed by these algorithms on balanced and imbalanced dataset for complex values and real values
