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首页> 外文期刊>Neurocomputing >Supervised immune clonal evolutionary classification algorithm for high-dimensional data
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Supervised immune clonal evolutionary classification algorithm for high-dimensional data

机译：高维数据的监督免疫克隆进化分类算法

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摘要

Classification is very difficult in high-dimensional spaces because learning methods suffer from the curse of dimensionality. In order to efficiently classify the high-dimensional data, a Supervised Immune Clonal Evolutionary Classification Algorithm (SICECA) is proposed in this paper. First, the automatic nonparametric Uncorrelated Discriminant Analysis (UDA) is adopted for Dimensionality Reduction (DR), which combines rank-preserving dimensionality reduction with constraint discriminant analysis so as to realize the extracted features statistically uncorrelated. Then, an Immune Clonal Evolutionary Algorithm (ICEA) based on clonal selection principle in immunology is proposed to act as classifier. In the experiments, first of all, 11 UCI data sets, four texture images and three Synthetic Aperture Radar (SAR) images are used to test the performance of SICECA. SICECA is also compared with three existing algorithms in terms of classification accuracy and running time. In addition, SICECA is applied to a real world application, namely, face recognition, with a good performance obtained.
机译：在高维空间中，分类是非常困难的，因为学习方法遭受了维数的诅咒。为了有效地对高维数据进行分类，提出了一种监督免疫克隆进化分类算法（SICECA）。首先，将自动非参数不相关判别分析（UDA）用于降维（DR），该方法将保留等级的降维与约束判别分析相结合，以实现提取的统计上不相关的特征。然后，提出了一种基于克隆选择原理的免疫学免疫克隆进化算法（ICEA）作为分类器。在实验中，首先，使用11个UCI数据集，四个纹理图像和三个合成孔径雷达（SAR）图像来测试SICECA的性能。在分类准确性和运行时间方面，还将SICECA与三种现有算法进行了比较。另外，SICECA被应用于现实世界的应用中，即面部识别，并获得了良好的性能。

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  • 来源
    《Neurocomputing》 |2012年第2012期|p.123-134|共12页
  • 作者

    Ruochen Liu; Ping Zhang; Licheng Jiao; Yangyang Li;

  • 作者单位

    Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xl'an 710071, China;

    Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xl'an 710071, China;

    Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xl'an 710071, China;

    Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xl'an 710071, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    dimensionality reduction; uncorrelated discriminant analysis (UDA); clonal selection principle; synthetic-aperture radar (SAR) image; face recognition;

    机译：降维;不相关的判别分析（UDA）;克隆选择原则合成孔径雷达（SAR）图像;人脸识别;

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