查询结果:   李艳芳,高大启.Fisher线性判别式阈值优化方法研究[J].计算机应用与软件,2016,33(6):141 - 145.
中文标题
Fisher线性判别式阈值优化方法研究
发表栏目
人工智能与识别
摘要点击数
413
英文标题
ON OPTIMISING THRESHOLDS OF FISHER LINEAR DISCRIMINANT
作 者
李艳芳 高大启 Li Yanfang Gao Daqi
作者单位
华东理工大学信息科学与工程学院 上海 200237     
英文单位
School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China     
关键词
分类 Fisher线性判别式 阈值 不平衡数据集
Keywords
Classification Fisher linear discriminant Thresholds Imbalanced dataset
基金项目
国家自然科学基金项目(21176077)
作者资料
李艳芳,硕士生,主研领域:模式识别。高大启,教授。 。
文章摘要
Fisher线性判别式FLDs(Fisher linear discriminates)的常用阈值对不平衡数据集分类效果较差。以不平衡数据集为应用背景,主要研究各种阈值对FLDs分类性能的影响。认为影响 FLDs性能的主要是类间分布区域不平衡而不是样本数不平衡,因此提出多个经验阈值,并依据分类精度从中选择优化阈值。大量实验结果表明,所提出的阈值优化选择方法能有效提高FLDs对不平衡数据集的分类性能。
Abstract
The commonly used thresholds of Fisher linear discriminant (FLD) always have poor classification result on imbalanced datasets. On application background of the imbalanced datasets, in this paper we mainly study the influence of various thresholds on FLD’s classification performance. We argue that for FLDs, it’s the imbalance of inter-class distribution regions rather than sample sizes that mainly impacts the performance of FLDs, and thus we develop several empirical thresholds and select the optimised thresholds based on classification accuracy. Extensive experimental results show that the classification performance of FLDs on imbalanced datasets is improved effectively with the use of the proposed optimised threshold selection method.
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