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)
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Abstract
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.
| Item Type: | Article |
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| Additional Information: | JCR® Category: Multidisciplinary Sciences. Quartile: Q2 |
| Uncontrolled Keywords: | Data Mining Technique; Qualitative Bankruptcy; Classification |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Faculty/Division: | Faculty of Computer System And Software Engineering |
| Depositing User: | Pn. Hazlinda Abd Rahman |
| Date Deposited: | 19 Feb 2018 05:17 |
| Last Modified: | 22 Nov 2018 01:57 |
| URI: | http://umpir.ump.edu.my/id/eprint/19748 |
| Download Statistic: | View Download Statistics |
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