Adaboost ensemble classifiers for corporate default prediction
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 d...
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| Main Authors: | , , |
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| Format: | Article |
| Published: |
Maxwell Science Publications
2015
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/57696/ |
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| Summary: | 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. |
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