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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학위논문
- 저자정보
- 발행연도
- 2014
- 저작권
- 경상대학교 논문은 저작권에 의해 보호받습니다.
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Bankruptcy prediction has been an important topic in the accounting and finance field for a long time. Several data mining techniques have been used for bankruptcy prediction. However, there are many limits for application to real classification problem with a single model.
This study proposes ensemble SVM (support vector machine) model which assembles different SVM models with each different kernel functions. Our ensemble model is made and evaluated by v-fold cross-validation approach. The top performing models are recruited into the ensemble. The classification is then carried out using the majority voting opinion of the ensemble.
The performance of the ensemble SVM classifier is investigated in terms of accuracy, error rate, sensitivity, specificity, ROC curve, and AUC to compare with single SVM classifiers based on two financial ratios datasets and simulation datasets. The results confirmed the advantages of our method: It is being robust while providing good performance.
This study proposes ensemble SVM (support vector machine) model which assembles different SVM models with each different kernel functions. Our ensemble model is made and evaluated by v-fold cross-validation approach. The top performing models are recruited into the ensemble. The classification is then carried out using the majority voting opinion of the ensemble.
The performance of the ensemble SVM classifier is investigated in terms of accuracy, error rate, sensitivity, specificity, ROC curve, and AUC to compare with single SVM classifiers based on two financial ratios datasets and simulation datasets. The results confirmed the advantages of our method: It is being robust while providing good performance.
목차
Ⅰ. 서론 1Ⅱ. SVM 모형 3Ⅲ. 교차 타당성을 이용한 앙상블 SVM 모형 61. v -조각 교차 타당성 62. 앙상블 SVM 모형 73. 모형 성능 평가 척도 8Ⅳ. 실험결과 및 논의사항 101. 기업의 재무비율 자료 101) Pietruszkiewicz dataset 102) Philippe du Jardin dataset 152. 모의실험 자료 211) 독립인 모의실험 자료 212) 종속인 모의실험 자료 25Ⅴ. 결론 30참고문헌 32부록 34
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