The imbalance dataset problem arises in many domains,such as web page search, scam sites detection. In this paper,we propose an alternative re-sampling approach to dealwith imbalance datasets. We demonstrate this approach with a concrete implementat
Abstract. Support Vector Machines (SVM) have been extensively studied and have shown remarkable success in many applications. However the success of SVM is very limited when it is applied to the problem of learning from imbalanced datasets in which
重新思考标签的价值，以改善班级不平衡的学习
该存储库包含纸张的实现代码：重新思考标签的价值，以改善班级不平衡的学习和2020年第三十四届神经信息处理系统会议（NeurIPS） [] [ ] [] [] []
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inproceedings { yang2020rethinking ,
title = { Rethinking the Value of Labels for Improving Class-Imbalanced Learnin
Driven by the need of a plethora of machine learning applications, several attempts have been made at improving the performance of classifiers applied to imbalanced datasets. In this paper, we present a fast maximum entropy machine(MEM)combined with
