A comparative analysis of four classification algorithms for university students performance detection
The student's performance plays an important role in producing the best quality graduate who will responsible for the country's economic growth and social de-velopment. Labor market also concern with student's performance because the fresh graduate students are considered as an employ...
| Main Authors: | , , , , , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Universiti Malaysia Pahang
2019
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/25983/1/29.%20A%20comparative%20analysis%20of%20four%20classification%20algorithms.pdf http://umpir.ump.edu.my/id/eprint/25983/2/29.1%20A%20comparative%20analysis%20of%20four%20classification%20algorithms.pdf |
| _version_ | 1796993571086139392 |
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| author | Dipta, Das Asif Khan, Shakir Sah Golam, Rabbani Mostafijur, Rahman Syamimi Mardiah, Shaharum Sabira, Khatun Norasyikin, Fadilah Khandker, M. Qaiduzzaman Md. Shariful, Islam Md. Shohel, Arman |
| author_facet | Dipta, Das Asif Khan, Shakir Sah Golam, Rabbani Mostafijur, Rahman Syamimi Mardiah, Shaharum Sabira, Khatun Norasyikin, Fadilah Khandker, M. Qaiduzzaman Md. Shariful, Islam Md. Shohel, Arman |
| author_sort | Dipta, Das |
| collection | UMP |
| description | The student's performance plays an important role in producing the best quality graduate who will responsible for the country's economic growth and social de-velopment. Labor market also concern with student's performance because the fresh graduate students are considered as an employee depends on their academic performance. So, identification the reason behind student’s performance variation provides a valuable information for planning education and policies. Many researchers try to find out the reason with different types of data mining approaches in different countries. But none of them worked with Bangladeshi students. This paper proposed a model for identifying the key factors of variation Bangladeshi students’ academic performance and predicts their results. This paper proposes a model which able to identify the students who need special attention. Different types of feature selection methods were used such as Co-relation, Chi-Square and Euclidean distance to select valuable features. And showing the comparison of feature selections result through decision tree, Naive Bayes, K-nearest neighbor and Artificial Neural Network classifiers algorithm. The performance analysis is done by using student SGPA and review on given facilities from a university. From the performance analysis result it is found that, decreasing number of classes in dataset, the Artificial Neural Network(ANN) (93.70%) performs better than Decision Tree(DT)(92.18%), K-Nearest Neighbors (KNN)(77.74%) and Naïve Bayes (NB)(68.33%). However, increasing number of classes in dataset the DT perform better then ANN, KNN, NB. |
| first_indexed | 2024-03-06T12:35:53Z |
| format | Conference or Workshop Item |
| id | UMPir25983 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-03-06T12:35:53Z |
| publishDate | 2019 |
| publisher | Universiti Malaysia Pahang |
| record_format | dspace |
| spelling | UMPir259832019-12-17T03:34:45Z http://umpir.ump.edu.my/id/eprint/25983/ A comparative analysis of four classification algorithms for university students performance detection Dipta, Das Asif Khan, Shakir Sah Golam, Rabbani Mostafijur, Rahman Syamimi Mardiah, Shaharum Sabira, Khatun Norasyikin, Fadilah Khandker, M. Qaiduzzaman Md. Shariful, Islam Md. Shohel, Arman TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering The student's performance plays an important role in producing the best quality graduate who will responsible for the country's economic growth and social de-velopment. Labor market also concern with student's performance because the fresh graduate students are considered as an employee depends on their academic performance. So, identification the reason behind student’s performance variation provides a valuable information for planning education and policies. Many researchers try to find out the reason with different types of data mining approaches in different countries. But none of them worked with Bangladeshi students. This paper proposed a model for identifying the key factors of variation Bangladeshi students’ academic performance and predicts their results. This paper proposes a model which able to identify the students who need special attention. Different types of feature selection methods were used such as Co-relation, Chi-Square and Euclidean distance to select valuable features. And showing the comparison of feature selections result through decision tree, Naive Bayes, K-nearest neighbor and Artificial Neural Network classifiers algorithm. The performance analysis is done by using student SGPA and review on given facilities from a university. From the performance analysis result it is found that, decreasing number of classes in dataset, the Artificial Neural Network(ANN) (93.70%) performs better than Decision Tree(DT)(92.18%), K-Nearest Neighbors (KNN)(77.74%) and Naïve Bayes (NB)(68.33%). However, increasing number of classes in dataset the DT perform better then ANN, KNN, NB. Universiti Malaysia Pahang 2019 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/25983/1/29.%20A%20comparative%20analysis%20of%20four%20classification%20algorithms.pdf pdf en http://umpir.ump.edu.my/id/eprint/25983/2/29.1%20A%20comparative%20analysis%20of%20four%20classification%20algorithms.pdf Dipta, Das and Asif Khan, Shakir and Sah Golam, Rabbani and Mostafijur, Rahman and Syamimi Mardiah, Shaharum and Sabira, Khatun and Norasyikin, Fadilah and Khandker, M. Qaiduzzaman and Md. Shariful, Islam and Md. Shohel, Arman (2019) A comparative analysis of four classification algorithms for university students performance detection. In: 5th International Conference on Electrical, Control and Computer Engineering (INECCE 2019) , 29-30 July 2019 , Swiss Garden Kuantan. pp. 1-12.. (Unpublished) DOI: https://doi.org/10.1007/s40684-019-00017-4 |
| spellingShingle | TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering Dipta, Das Asif Khan, Shakir Sah Golam, Rabbani Mostafijur, Rahman Syamimi Mardiah, Shaharum Sabira, Khatun Norasyikin, Fadilah Khandker, M. Qaiduzzaman Md. Shariful, Islam Md. Shohel, Arman A comparative analysis of four classification algorithms for university students performance detection |
| title | A comparative analysis of four classification algorithms for university students performance detection |
| title_full | A comparative analysis of four classification algorithms for university students performance detection |
| title_fullStr | A comparative analysis of four classification algorithms for university students performance detection |
| title_full_unstemmed | A comparative analysis of four classification algorithms for university students performance detection |
| title_short | A comparative analysis of four classification algorithms for university students performance detection |
| title_sort | comparative analysis of four classification algorithms for university students performance detection |
| topic | TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/25983/1/29.%20A%20comparative%20analysis%20of%20four%20classification%20algorithms.pdf http://umpir.ump.edu.my/id/eprint/25983/2/29.1%20A%20comparative%20analysis%20of%20four%20classification%20algorithms.pdf |
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