Comparison Performance of Qualitative Bankruptcy Classification based on Data Mining Algorithms
Bankruptcy classification and prediction are imperative for informed decision making and problem-solving in actual risk assessment. Knowledge discovery using data mining techniques are commonly applied in bankruptcy classification and prediction. This paper presents a comparison of three different c...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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American Scientific Publisher
2018
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| Online Access: | http://umpir.ump.edu.my/id/eprint/19748/ http://umpir.ump.edu.my/id/eprint/19748/1/50.%20Comparison%20Performance%20of%20Qualitative%20Bankruptcy%20Classification%20based%20on%20Data%20Mining%20Algorithms1.pdf |
| _version_ | 1848820953905102848 |
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| author | Nilam Nur Amir, Sjarif Yee, Fang Lim NurulHuda, Mohd Firdaus Azmi Kamalia, Kamardin Doris Wong, Hooi Ten Hafiza, Abas Mubarak-Ali, Al-Fahim |
| author_facet | Nilam Nur Amir, Sjarif Yee, Fang Lim NurulHuda, Mohd Firdaus Azmi Kamalia, Kamardin Doris Wong, Hooi Ten Hafiza, Abas Mubarak-Ali, Al-Fahim |
| author_sort | Nilam Nur Amir, Sjarif |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Bankruptcy classification and prediction are imperative for informed decision making and problem-solving in actual risk assessment. Knowledge discovery using data mining techniques are commonly applied in bankruptcy classification and prediction. This paper presents a comparison of three different classification algorithms namely NaiveBayes (NaiveBayes classifier), Logistic Regression (Logistic classifier) and C4.5 decision tree (J48 classifier) for bankruptcy classification analysis. Qualitative bankruptcy data retrieved from UCI Machine Learning Repository is used for the experimental study. The paper adopted percentage split and cross validation methods for more precise results of the classification performance. The results of the experiment show that NaiveBayes classifier has higher accuracy compares to Logistic and J48 classifiers. The paper contributes as a reference in high accuracy classifier selection for more effective decision supports in solving bankruptcy classification problems. |
| first_indexed | 2025-11-15T02:17:39Z |
| format | Article |
| id | ump-19748 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:17:39Z |
| publishDate | 2018 |
| publisher | American Scientific Publisher |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-197482018-11-22T01:57:41Z http://umpir.ump.edu.my/id/eprint/19748/ Comparison Performance of Qualitative Bankruptcy Classification based on Data Mining Algorithms Nilam Nur Amir, Sjarif Yee, Fang Lim NurulHuda, Mohd Firdaus Azmi Kamalia, Kamardin Doris Wong, Hooi Ten Hafiza, Abas Mubarak-Ali, Al-Fahim QA76 Computer software Bankruptcy classification and prediction are imperative for informed decision making and problem-solving in actual risk assessment. Knowledge discovery using data mining techniques are commonly applied in bankruptcy classification and prediction. This paper presents a comparison of three different classification algorithms namely NaiveBayes (NaiveBayes classifier), Logistic Regression (Logistic classifier) and C4.5 decision tree (J48 classifier) for bankruptcy classification analysis. Qualitative bankruptcy data retrieved from UCI Machine Learning Repository is used for the experimental study. The paper adopted percentage split and cross validation methods for more precise results of the classification performance. The results of the experiment show that NaiveBayes classifier has higher accuracy compares to Logistic and J48 classifiers. The paper contributes as a reference in high accuracy classifier selection for more effective decision supports in solving bankruptcy classification problems. American Scientific Publisher 2018-11 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/19748/1/50.%20Comparison%20Performance%20of%20Qualitative%20Bankruptcy%20Classification%20based%20on%20Data%20Mining%20Algorithms1.pdf Nilam Nur Amir, Sjarif and Yee, Fang Lim and NurulHuda, Mohd Firdaus Azmi and Kamalia, Kamardin and Doris Wong, Hooi Ten and Hafiza, Abas and Mubarak-Ali, Al-Fahim (2018) Comparison Performance of Qualitative Bankruptcy Classification based on Data Mining Algorithms. Advanced Science Letters, 24 (10). pp. 7602-7606. ISSN 1936-6612. (Published) https://doi.org/10.1166/asl.2018.12986 doi: 10.1166/asl.2018.12986 |
| spellingShingle | QA76 Computer software Nilam Nur Amir, Sjarif Yee, Fang Lim NurulHuda, Mohd Firdaus Azmi Kamalia, Kamardin Doris Wong, Hooi Ten Hafiza, Abas Mubarak-Ali, Al-Fahim Comparison Performance of Qualitative Bankruptcy Classification based on Data Mining Algorithms |
| title | Comparison Performance of Qualitative Bankruptcy Classification based on Data Mining Algorithms |
| title_full | Comparison Performance of Qualitative Bankruptcy Classification based on Data Mining Algorithms |
| title_fullStr | Comparison Performance of Qualitative Bankruptcy Classification based on Data Mining Algorithms |
| title_full_unstemmed | Comparison Performance of Qualitative Bankruptcy Classification based on Data Mining Algorithms |
| title_short | Comparison Performance of Qualitative Bankruptcy Classification based on Data Mining Algorithms |
| title_sort | comparison performance of qualitative bankruptcy classification based on data mining algorithms |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/19748/ http://umpir.ump.edu.my/id/eprint/19748/ http://umpir.ump.edu.my/id/eprint/19748/ http://umpir.ump.edu.my/id/eprint/19748/1/50.%20Comparison%20Performance%20of%20Qualitative%20Bankruptcy%20Classification%20based%20on%20Data%20Mining%20Algorithms1.pdf |