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陈凌驰IEEE信息科技和技术会议报告

来源: 点击: 时间: 2018年09月19日 15:57

会议名称:CyberSciTech 2018

全称:IEEE信息科技和技术会议(IEEE Cyber Science and Technology Congress)

报告时间:2018年9月20日(星期二)下午4:00

报告地点:铁道校区综合实验楼308会议室

报告人:陈凌驰

报告题目:DYCUSBoost: Adaboost-based imbalanced learning using dynamic clustering and undersampling

报告摘要:

Ensemble learning is a powerful approach to classifying imbalanced data in machine learning. Adaboost as one of Ensemble learning, which often modified to deal with imbalanced problem. However, due to the variation of sample weights in Adaboost algorithm, the distribution of datasets is not consistent for each weak classifier. As a result, feature space-based resampling fails to reflect the transformation of distribution. Aiming at this problem, this paper proposes DYCUSBoost, an Adaboost-based imbalanced learning approach using dynamic clustering and undersampling. In DYCUSBoost, the clustering process is synchronized with the iteration of Adaboost, where clusters formed in different periods of Adaboost are adjusted, which make DYCUSBoost grasp the transformation of the distribution. The undersampling method assesses the importance of each cluster, and make important ones collect more samples. Through experimental verification, DYCUSBoost demonstrates desirable performance in terms of commonly-accepted evaluating metrics, e.g., AUC, G-Mean, F-Measure, etc. Moreover, the prediction stability of DYCUSBoost outperforms most undersampling methods.


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