Earticle

초록

영어

This paper proposes a novel geometric mean (GM) optimization-based boosting algorithm (GMOPTBoost) to improve the performance of boosting ensembles applied to solve the class imbalance problem in bankruptcy prediction. GMOPTBoost derives the best prediction by applying Gaussian gradient descent method to find the set of weights assigned to base classifiers to optimize GM. The main findings are as follows. First, the class imbalance problem has a negative effect on the performance. As IR values increase, the performances of boosting ensembles decreases. Second, GMOPTBoost makes a significant contribution to performance improvements of AdaBoost ensembles trained on imbalanced datasets.

목차

Abstract
Introduction
Learning Algorithms
Neural networks base classifiers
GMOPTBoost algorithm
Experimental Setup and Results
Sample and variable selection
Acknowledgments
References

키워드

저자

• Myoung Jong Kim [ Faculty of School of Business,Pusan National University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2026

• 십진분류

  KDC 325 DDC 658