사례 선택 기법을 활용한 앙상블 모형의 성능 개선

• 영문명: Improving an Ensemble Model Using Instance Selection Method
• 발행기관: 한국산업경영시스템학회
• 저자명: 민성환(Sung Hwan Min)
• 간행물 정보: 『산업경영시스템학회지』제39권 제1호, 105~115쪽, 전체 11쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2016.03.30

영문 초록

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 demon-strated that the proposed model outperformed other models including the single model, the instance selection model, and the original random subspace ensemble model.

목차

Abstract

1. 서론

2. 문헌 연구

3. 연구 모형

4. 실험 설계

5. 실험 결과

6. 결론

References

키워드

Random Subspace, Bagging, Bankruptcy Prediction, Genetic Algorithms