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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2016.1
- 수록면
- 105 - 115 (11page)
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Ensemble classification involves combining individually trained classifiers to yield more accurate prediction, compared with individual models. Ensemble techniques are very useful for improving the generalization ability of classifiers. The random subspace ensemble technique is a simple but effective method for constructing ensemble classifiers; it involves randomly drawing some of the features from each classifier in the ensemble. The instance selection technique involves selecting critical instances while deleting and removing irrelevant and noisy instances from the original dataset. The instance selection and random subspace methods are both well known in the field of data mining and have proven to be very effective in many applications. However, few studies have focused on integrating the instance selection and random subspace methods. Therefore, this study proposed a new hybrid ensemble model that integrates instance selection and random subspace techniques using genetic algorithms (GAs) to improve the performance of a random subspace ensemble model. GAs are used to select optimal (or near optimal) instances, which are used as input data for the random subspace ensemble model. The proposed model was applied to both Kaggle credit data and corporate credit data, and the results were compared with those of other models to investigate performance in terms of classification accuracy, levels of diversity, and average classification rates of base classifiers in the ensemble. The experimental results demonstrated that the proposed model outperformed other models including the single model, the instance selection model, and the original random subspace ensemble model.
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참고문헌 (35)
참고문헌 신청
Abellan, J. / 2014 / Improving Experimental Studies about Ensembles of Classifiers for Bankruptcy Prediction and Credit Scoring / Expert Systems with Applications 41(8):3825~3830
Altman, E. L. / 1968 / Financial ratios, discriminant analysis and the prediction of corporate bankruptcy / The Journal of Finance 23(4):589~609
Beaver, W. / 1966 / Financial ratios as predictors of failure, empirical research in accounting : Selected studied / Journal of Accounting Research 4(3):71~111
Bian, S. / 2007 / On diversity and accuracy of homogeneous and heterogeneous ensembles / International Journal of Hybrid Intelligent Systems 4(2):103~128
Breiman, L. / 1996 / Bagging predictors / Machine Learning 24(2):123~140
Altman, E. L. / 1968 / Financial ratios, discriminant analysis and the prediction of corporate bankruptcy / The Journal of Finance 23(4):589~609
Beaver, W. / 1966 / Financial ratios as predictors of failure, empirical research in accounting : Selected studied / Journal of Accounting Research 4(3):71~111
Bian, S. / 2007 / On diversity and accuracy of homogeneous and heterogeneous ensembles / International Journal of Hybrid Intelligent Systems 4(2):103~128
Breiman, L. / 1996 / Bagging predictors / Machine Learning 24(2):123~140
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