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Article Information:
Adaboost Ensemble Classifiers for Corporate Default Prediction
Suresh Ramakrishnan, Maryam Mirzaei and Mahmoud Bekri
Corresponding Author: Maryam Mirzaei
Submitted: October 05, 2014
Accepted: November 10, 2014
Published: January 25, 2015 |
Abstract:
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This study aims to show a substitute technique to corporate default prediction. Data mining techniques have been extensively applied for this task, due to its ability to notice non-linear relationships and show a good performance in presence of noisy information, as it usually happens in corporate default prediction problems. In spite of several progressive methods that have widely been proposed, this area of research is not out dated and still needs further examination. In this study, the performance of multiple classifier systems is assessed in terms of their capability to appropriately classify default and non-default Malaysian firms listed in Bursa Malaysia. Multi-stage combination classifiers provided significant improvements over the single classifiers. In addition, Adaboost shows improvement in performance over the single classifiers.
Key words: Data Mining, default prediction, ensemble classifier, , , ,
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Cite this Reference:
Suresh Ramakrishnan, Maryam Mirzaei and Mahmoud Bekri, . Adaboost Ensemble Classifiers for Corporate Default Prediction. Research Journal of Applied Sciences, Engineering and Technology, (3): 224-230.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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