ENHANCED PREDICTION OF STUDENT DROPOUTS USING FUZZY INFERENCE SYSTEM AND LOGISTIC REGRESSION
Predicting college and school dropouts is a major problem in educational system and has complicated challenge due to data imbalance and multi dimensionality, which can affect the low performance of students. In this paper, we have collected different database from various colleges, among these 500 b...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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ICT Academy of Tamil Nadu
2016-01-01
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| Series: | ICTACT Journal on Soft Computing |
| Subjects: | |
| Online Access: | http://ictactjournals.in/paper/IJSC_Vol_6_Iss_2_paper_7_1157_1162.pdf |
| _version_ | 1818995760651304960 |
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| author | A. Saranya J. Rajeswari |
| author_facet | A. Saranya J. Rajeswari |
| author_sort | A. Saranya |
| collection | DOAJ |
| description | Predicting college and school dropouts is a major problem in educational system and has complicated challenge due to data imbalance and multi dimensionality, which can affect the low performance of students. In this paper, we have collected different database from various colleges, among these 500 best real attributes are identified in order to identify the factor that affecting dropout students using neural based classification algorithm and different mining technique are implemented for data processing. We also propose a Dropout Prediction Algorithm (DPA) using fuzzy logic and Logistic Regression based inference system because the weighted average will improve the performance of whole system. We are experimented our proposed work with all other classification systems and documented as the best outcomes. The aggregated data is given to the decision trees for better dropout prediction. The accuracy of overall system 98.6% it shows the proposed work depicts efficient prediction. |
| first_indexed | 2024-12-20T21:18:58Z |
| format | Article |
| id | doaj.art-e8b2724d942549ce86a162be4fcc61dc |
| institution | Directory Open Access Journal |
| issn | 0976-6561 2229-6956 |
| language | English |
| last_indexed | 2024-12-20T21:18:58Z |
| publishDate | 2016-01-01 |
| publisher | ICT Academy of Tamil Nadu |
| record_format | Article |
| series | ICTACT Journal on Soft Computing |
| spelling | doaj.art-e8b2724d942549ce86a162be4fcc61dc2022-12-21T19:26:21ZengICT Academy of Tamil NaduICTACT Journal on Soft Computing0976-65612229-69562016-01-016211571162ENHANCED PREDICTION OF STUDENT DROPOUTS USING FUZZY INFERENCE SYSTEM AND LOGISTIC REGRESSIONA. Saranya0J. Rajeswari1Adhiparasakthi Engineering College, IndiaAdhiparasakthi Engineering College, IndiaPredicting college and school dropouts is a major problem in educational system and has complicated challenge due to data imbalance and multi dimensionality, which can affect the low performance of students. In this paper, we have collected different database from various colleges, among these 500 best real attributes are identified in order to identify the factor that affecting dropout students using neural based classification algorithm and different mining technique are implemented for data processing. We also propose a Dropout Prediction Algorithm (DPA) using fuzzy logic and Logistic Regression based inference system because the weighted average will improve the performance of whole system. We are experimented our proposed work with all other classification systems and documented as the best outcomes. The aggregated data is given to the decision trees for better dropout prediction. The accuracy of overall system 98.6% it shows the proposed work depicts efficient prediction.http://ictactjournals.in/paper/IJSC_Vol_6_Iss_2_paper_7_1157_1162.pdfData MiningFuzzy Inference SystemLogistic RegressionDecision TreesStudent Dropout |
| spellingShingle | A. Saranya J. Rajeswari ENHANCED PREDICTION OF STUDENT DROPOUTS USING FUZZY INFERENCE SYSTEM AND LOGISTIC REGRESSION ICTACT Journal on Soft Computing Data Mining Fuzzy Inference System Logistic Regression Decision Trees Student Dropout |
| title | ENHANCED PREDICTION OF STUDENT DROPOUTS USING FUZZY INFERENCE SYSTEM AND LOGISTIC REGRESSION |
| title_full | ENHANCED PREDICTION OF STUDENT DROPOUTS USING FUZZY INFERENCE SYSTEM AND LOGISTIC REGRESSION |
| title_fullStr | ENHANCED PREDICTION OF STUDENT DROPOUTS USING FUZZY INFERENCE SYSTEM AND LOGISTIC REGRESSION |
| title_full_unstemmed | ENHANCED PREDICTION OF STUDENT DROPOUTS USING FUZZY INFERENCE SYSTEM AND LOGISTIC REGRESSION |
| title_short | ENHANCED PREDICTION OF STUDENT DROPOUTS USING FUZZY INFERENCE SYSTEM AND LOGISTIC REGRESSION |
| title_sort | enhanced prediction of student dropouts using fuzzy inference system and logistic regression |
| topic | Data Mining Fuzzy Inference System Logistic Regression Decision Trees Student Dropout |
| url | http://ictactjournals.in/paper/IJSC_Vol_6_Iss_2_paper_7_1157_1162.pdf |
| work_keys_str_mv | AT asaranya enhancedpredictionofstudentdropoutsusingfuzzyinferencesystemandlogisticregression AT jrajeswari enhancedpredictionofstudentdropoutsusingfuzzyinferencesystemandlogisticregression |