Performance Evaluation by Fuzzy Inference Technique
Shruti S Jamsandekar1, R.R Mudholkar2
1Mrs. Shruti S. Jamsandekar Department of Computer Studies, SIBER, Kolhapur. Maharashtra, India.
2Dr. Ravindra R. Mudholkar Department of Electronics, Shivaji University, Maharashtra, India.
Manuscript received on April 03, 2013. | Revised Manuscript received on April 29, 2013. | Manuscript published on May 05, 2013. | PP: 158-164 | Volume-3, Issue-2, May 2013. | Retrieval Number: B1477053213/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The education domain offers a fertile ground for many interesting and challenging data mining applications. These applications can help both educators and students, and improve the quality of education. The ability to monitor the progress of student’s academic performance is a critical issue to the academic community of higher learning. The present work intends to approach this problem by taking the advantage of fuzzy inference technique in order to classify student scores data according to the level of their performance In this proposed approach we have performed fuzzification of the input data( students marks) by creating fuzzy inference system(FIS) subject wise, next each FIS output is passed to next level FIS with two inputs, outputs of the final FIS are performance value calculated based on all subject marks with/without lab marks. In the proposed approached a combination of two membership function is carried out (trapezoidal and triangular).The experimental results are compared with traditional evaluation method, it helps in identifying students lying at overlapping section of two class distribution the results also could help educators to monitor the progress and provide timely guidance to students to achieve better performance score.
Keywords: Performance Evaluation, Academic Institute, Fuzzy Classification, Fuzzy Inference.