Classification for imbalanced dataset based on biased empirical feature mapping

机译：基于偏差经验特征映射的不平衡数据集分类

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It is shown that an imbalanced datasets can pose serious problems to many real-world classification tasks when support vector machines is used as the learning machine. To solve this problem, we propose a modified method based on biased empirical feature mapping. In the new method, biased discriminant analysis was applied to make all majority samples far away from center of minority samples in empirical feature space, so that generalization ability of the classifier for minority samples can be improved. Through theoretical analysis and empirical study on synthetic datasets and UCI datasets, we show that our method augments the classification accuracy rate effectively.

机译：结果表明，当使用支持向量机作为学习机时，不平衡的数据集会给许多现实世界中的分类任务带来严重问题。为了解决这个问题，我们提出了一种基于有偏经验特征映射的改进方法。在新方法中，通过偏倚判别分析使所有多数样本在经验特征空间内远离少数样本中心，从而提高分类器对少数样本的泛化能力。通过对合成数据集和UCI数据集的理论分析和实证研究，我们表明该方法有效地提高了分类准确率。

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来源
《Instrumentation and Measurement Technology Conference (I2MTC), 2012 IEEE International》|2012年|p.1645- 1649|共5页
会议地点 Graz(AT)
作者
Yang Zhiming; Yang Yu; Gang Wang;
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作者单位

Department of Automatic Test and Control, Harbin Institute of Technology, Heilongjiang Province, China;

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原文格式 PDF
正文语种 eng
中图分类计量学;
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Classification for imbalanced dataset based on biased empirical feature mapping

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