Comparative Analysis of Features Extraction Strategies for Classification in Educational Data Mining
P. Kavipriya1, K. Karthikeyan2

1P. Kavipriya, Assistant Professor, Department of Computer Science, Sri Ramakrishna College of Arts and Science, Coimbatore (Tamil Nadu), India..
2Dr. K. Karthikeyan, Head, Department of Computer Science, Government Arts College, Palladam (Tamil Nadu), India..
Manuscript received on 24 April 2019 | Revised Manuscript received on 02 May 2019 | Manuscript Published on 08 May 2019 | PP: 458-463 | Volume-7 Issue-5S3 February 2019 | Retrieval Number: E11810275S19/19©BEIESP
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Abstract: In this era of automation, education has also smartened up itself and is not imperfect to old lecture method. The expected search is on to find out innovative ways to build it more useful and proficient in developing students’ performance. Currently, huge of data are gathered in educational databases, other than it remains unutilized. In order to obtain essential benefits from such big data, strong tools are required. Data mining is alarmed with the development of methods and techniques for making use of data analysis and prediction. This is the course of pattern detection as well as extraction where the vast amount of data is concerned. Both the data mining and education industry have emerged some of the reliable systems. In regard to this emerge; we present an approach for handling feature extraction by utilizing data mining algorithms for educational system and systematically evaluate the performance of the algorithms to find best-fit features for the further classification process. Results are discussed for selected papers and a summary of the finding is presented to conclude the paper.
Keywords: Feature Extraction, Genetic Algorithm, Feature Selection, Classifiers, Support Vector Machine.
Scope of the Article: Data Mining