• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of applied evolutionary computation >A Hybrid Kernel Extreme Learning Machine and Improved Cat Swarm Optimization for Microarray Medical Data Classification
【24h】

A Hybrid Kernel Extreme Learning Machine and Improved Cat Swarm Optimization for Microarray Medical Data Classification

机译：混合核极限学习机和改进的Cat群优化算法在微阵列医学数据分类中的应用

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper presents the pattern classification of the binary microarray gene expression based medical data using extreme learning machine (ELM) and its variants like on-line sequential ELM (OSELM) and kernel based extreme learning machine (KELM). In the KELM category two variants namely the wavelet based kernel (WKELM) extreme learning machine and radial basis kernel extreme learning machine (RKELM) along with support vector machine (SVMRBF) and support vector machine polynomial (SVMPoly) are used to classify microarray medical datasets. Further to reduce the high dimensionality of Microarray medical datasets giving rise to high number of gene expression and small sample sizes, a modified evolutionary cat swarm optimization (MCSO) technique is adopted. The efficiency of the proposed algorithm is verified using a set of performance metrics for four binary medical datasets belonging to breast cancer, prostate cancer, colon tumor, and leukemia, respectively.
机译：本文介绍了使用极限学习机（ELM）及其基于在线顺序ELM（OSELM）和基于内核的极限学习机（KELM）等变体对基于二进制微阵列基因表达的医学数据进行模式分类的方法。在KELM类别中，两个变体分别是基于小波的内核（WKELM）极限学习机和基于径向基核的极限学习机（RKELM）以及支持向量机（SVMRBF）和支持向量机多项式（SVMPoly）来对微阵列医学数据集进行分类。为了进一步减少微阵列医学数据集的高维性，从而导致基因表达数量大和样本量小，采用了改进的进化猫群优化（MCSO）技术。使用一组性能指标针对分别属于乳腺癌，前列腺癌，结肠癌和白血病的四个二进制医学数据集验证了所提出算法的效率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号