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Predicting Corporate Bankruptcy using Simulated Annealing-based Random Forests
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Hoyeon Park (Dongguk University_Seoul)
Kyoung-jae Kim (Dongguk University_Seoul)
Vol. 24, No. 4, Page: 155 ~ 170
10.13088/jiis.2018.24.4.155
Keywords
Simulated Annealing, Random Forests, Bankruptcy Prediction, Feature Selection, Business Analytics
Abstract
Predicting a company's financial bankruptcy is traditionally one of the most crucial forecasting problems in business analytics. In previous studies, prediction models have been proposed by applying or combining statistical and machine learning-based techniques. In this paper, we propose a novel intelligent prediction model based on the simulated annealing which is one of the well-known optimization techniques. The simulated annealing is known to have comparable optimization performance to the genetic algorithms. Nevertheless, since there has been little research on the prediction and classification of business decision-making problems using the simulated annealing, it is meaningful to confirm the usefulness of the proposed model in business analytics. In this study, we use the combined model of simulated annealing and machine learning to select the input features of the bankruptcy prediction model. Typical types of combining optimization and machine learning techniques are feature selection, feature weighting, and instance selection. This study proposes a combining model for feature selection, which has been studied the most. In order to confirm the superiority of the proposed model in this study, we apply the real-world financial data of the Korean companies and analyze the results. The results show that the predictive accuracy of the proposed model is better than that of the naïve model. Notably, the performance is significantly improved as compared with the traditional decision tree, random forests, artificial neural network, SVM, and logistic regression analysis.
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시뮬레이티드 어니일링 기반의 랜덤 포레스트를이용한 기업부도예측
박호연 (동국대학교)
김경재 (동국대학교)
Keywords
시뮬레이티드 어니일링, 랜덤 포레스트, 부도예측, 특징선택, 비즈니스 애널리틱스
Abstract
기업의 금융 부도를 예측하는 것은 전통적으로 비즈니스 분석에서 가장 중요한 예측문제 중 하나이다. 선행연구에서 예측모델은 통계 및 기계학습 기반의 기법을 적용하거나 결합하는 방식으로 제안되었다. 본 논문에서는 잘 알려진 최적화기법 중 하나인 시뮬레이티드 어니일링에 기반한 새로운 지능형예측모델을 제안한다. 시뮬레이티드 어니일링은 유전자알고리즘과 유사한 최적화 성능을 가진 것으로알려져 있다. 그럼에도 불구하고, 시뮬레이티드 어니일링을 사용한 비즈니스 의사결정 문제의 예측과분류에 관한 연구가 거의 없었기 때문에, 비즈니스 분석에서의 유용성을 확인하는 것은 의미가 있다.
본 연구에서는 시뮬레이티드 어니일링과 기계학습의 결합 모델을 사용하여 부도예측모델의 입력 특징을 선정한다. 최적화 기법과 기계학습기법을 결합하는 대표적인 유형은 특징 선택, 특징 가중치 및 사례 선택이다. 이 연구에서는 선행연구에서 가장 많이 연구된 특징 선택을 위한 결합모델을 제안한다.제안하는 모델의 우수성을 확인하기 위하여 본 연구에서는 한국 기업의 실제 재무데이터를 이용하여그 결과를 분석한다. 분석결과는 제안된 모델의 예측 정확도가 단순한 모델의 예측 정확성보다 우수하다는 것을 보여준다. 특히 기존의 의사결정나무, 랜덤포레스트, 인공신경망, SVM 및 로지스틱 회귀분석에 비해 분류성능이 향상되었다.
Cite this article
JIIS Style
Park, H., and K.-j. Kim, "Predicting Corporate Bankruptcy using Simulated Annealing-based Random Forests", Journal of Intelligence and Information Systems, Vol. 24, No. 4 (2018), 155~170.

IEEE Style
Hoyeon Park, and Kyoung-jae Kim, "Predicting Corporate Bankruptcy using Simulated Annealing-based Random Forests", Journal of Intelligence and Information Systems, vol. 24, no. 4, pp. 155~170, 2018.

ACM Style
Park, H., and Kim, K.-j., 2018. Predicting Corporate Bankruptcy using Simulated Annealing-based Random Forests. Journal of Intelligence and Information Systems. 24, 4, 155--170.
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@article{Park:JIIS:2018:754,
author = {Park, Hoyeon and Kim, Kyoung-jae},
title = {Predicting Corporate Bankruptcy using Simulated Annealing-based Random Forests},
journal = {Journal of Intelligence and Information Systems},
issue_date = {December 2018},
volume = {24},
number = {4},
month = Dec,
year = {2018},
issn = {2288-4866},
pages = {155--170},
url = {http://dx.doi.org/10.13088/jiis.2018.24.4.155 },
doi = {10.13088/jiis.2018.24.4.155},
publisher = {Korea Intelligent Information System Society},
address = {Seoul, Republic of Korea},
keywords = { Simulated Annealing, Random Forests, Bankruptcy Prediction, Feature Selection and Business Analytics
},
}
%0 Journal Article
%1 754
%A Hoyeon Park
%A Kyoung-jae Kim
%T Predicting Corporate Bankruptcy using Simulated Annealing-based Random Forests
%J Journal of Intelligence and Information Systems
%@ 2288-4866
%V 24
%N 4
%P 155-170
%D 2018
%R 10.13088/jiis.2018.24.4.155
%I Korea Intelligent Information System Society