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Geometric Structural Ensemble Learning for Imbalanced Problems
IEEE Transactions on Cybernetics ( IF 11.8 ) Pub Date : 2020-04-01 , DOI: 10.1109/tcyb.2018.2877663
Zonghai Zhu , Zhe Wang , Dongdong Li , Yujin Zhu , Wenli Du

The classification on imbalanced data sets is a great challenge in machine learning. In this paper, a geometric structural ensemble (GSE) learning framework is proposed to address the issue. It is known that the traditional ensemble methods train and combine a series of basic classifiers according to various weights, which might lack the geometric meaning. Oppositely, the GSE partitions and eliminates redundant majority samples by generating hyper-sphere through the Euclidean metric and learns basic classifiers to enclose the minority samples, which achieves higher efficiency in the training process and seems easier to understand. In detail, the current weak classifier builds boundaries between the majority and the minority samples and removes the former. Then, the remaining samples are used to train the next. When the training process is done, all of the majority samples could be cleaned and the combination of all basic classifiers is obtained. To further improve the generalization, two relaxation techniques are proposed. Theoretically, the computational complexity of GSE could approach $ {O(nd\log (n_{\min })\log (n_{\mathrm{ maj}}))}$ . The comprehensive experiments validate both the effectiveness and efficiency of GSE.

中文翻译:

几何结构集成学习的不平衡问题

不平衡数据集的分类在机器学习中是一个巨大的挑战。本文提出了一种几何结构集成(GSE)学习框架来解决这个问题。众所周知,传统的合奏方法会根据各种权重训练和组合一系列基本的分类器,这可能缺乏几何意义。相反,GSE通过欧几里德度量生成超球体来划分并消除多余的多数样本,并学习基本分类器来封装少数样本,这在训练过程中实现了更高的效率,并且似乎更易于理解。详细地说,当前的弱分类器在多数样本和少数样本之间建立了边界,并删除了前者。然后,剩余的样本将用于训练下一个样本。完成培训过程后,可以清洗所有大多数样品,并获得所有基本分类器的组合。为了进一步提高通用性,提出了两种松弛技术。从理论上讲,GSE的计算复杂度可以接近$ {O(nd \ log(n _ {\ min})\ log(n _ {\ mathrm {maj}}))} $。全面的实验验证了GSE的有效性和效率。
更新日期:2020-04-01
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