원문정보
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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
Introduction
Learning Algorithms
Neural networks base classifiers
GMOPTBoost algorithm
Experimental Setup and Results
Sample and variable selection
Acknowledgments
References
저자정보
참고문헌
자료제공 : 네이버학술정보