基于邻域搜索的在线特征大数据分类方法

李越颖

Chinese

您当前的位置：

首页 >

文章列表页 >

基于邻域搜索的在线特征大数据分类方法

计算机工程与应用 | 更新时间：2026-09-04

- 基于邻域搜索的在线特征大数据分类方法
- Big data classification method of neighborhood search for online feature selection
- 微电子学与计算机 2021年38卷第9期页码：61-66
- 作者机构：
  
  信阳农林学院机构信息工程学院,河南,信阳,464000
- 作者简介：
- 基金信息：
  
  河南省科技攻关项目 172102210450
- DOI：
  中图分类号：
- 纸质出版：2021
- 稿件说明：
移动端阅览
李越颖. 基于邻域搜索的在线特征大数据分类方法[J]. 微电子学与计算机, 2021, 38(9): 61-66.

LI Yueying. Big data classification method of neighborhood search for online feature selection[J]. 2021, 38(9): 61-66.
李越颖. 基于邻域搜索的在线特征大数据分类方法[J]. 微电子学与计算机, 2021, 38(9): 61-66. DOI：

LI Yueying. Big data classification method of neighborhood search for online feature selection[J]. 2021, 38(9): 61-66. DOI：

摘要

针对现有算法在处理海量数据集时处理效率低的问题，提出一种基于邻域搜索的在线特征选择(neighborhood search for online feature selection

NSOFS)并行大数据分类方法.在Map阶段，将大数据集进行分块，针对动态未知特征空间，通过萤火虫算法和模拟退火算法的优化，对于在线特征进行邻域搜索，选择最佳特征集，将获得的特征集作为Reduce阶段输入特征，然后使用内核支持向量机(Kernel Support Vector Machine

KSVM)对数据分类.实验结果表明：所提方法在精确率、召回率、F值和时间等性能方面优于其他现有方法.

Abstract

In order to solve the problem of low efficiency of existing algorithms when dealing with massive data sets

a parallel big data classification method is proposed based on neighborhood search for online feature selection. In the Map phase

the big data set is divided into splits. And the dynamic unknown feature space is optimized by the firefly algorithm and simulated annealing algorithm

the neighborhood search is carried out for the online features

then selecting the best feature. The obtained feature set is used as the input feature of the Reduce stage

and then the kernel support vector machine is used to classify the data. Experimental results show that the proposed method is superior to other existing methods in terms of accuracy

recall

F value and time.

关键词

Keywords

references

ZHANG Q C, YANG L T, CHEN Z K, et al. A survey on deep learning for big data[J]. Information Fusion, 2018(42): 146-157. DOI: 10.1016/j.inffus.2017.10.006.

李佳烨, 余浩. 基于K近邻的众包数据分类算法[J]. 计算机应用研究, 2020, 37(4): 973-976. DOI: 10.1 9734/j.issn.1001-3695.2018.09.0736. LI J Y, YU H. Crowdsourcing data classification algorithm via K-nearest neighbor[J]. Application Research of Computers, 2020, 37(4): 973-976. DOI: 10.1 9734/j.issn.1001-3695.2018.09.0736.

RAMREZ-GALLEGO S, FERNNDEZ A, GARCA S, et al. Big data: tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce[J]. Information Fusion, 2018, 42: 51-61. DOI: 10.1016/j.inffus.2017.10.001.

熊安萍, 蒋亚雄, 段杭彪, 等. 大数据环境下基于限定模糊规则的多分类器[J]. 计算机工程与设计, 2019, 40(4): 1145-1150. DOI: 10.16208/j.issn1000-7024.2019.04.039. XIONG A P, JIANG Y X, DUAN H B, et al. Multi-classifier based on limited fuzzy rules in big data[J]. Computer Engineering and Design, 2019, 40(4): 1145-1150. DOI: 10.16208/j.issn1000-7024.2019.04.039.

OUSSOUS A, BENJELLOUN F Z, LAHCEN A A, et al. Big data technologies: a survey[J]. Journal of King Saud University-Computer and Information Sciences, 2018, 30(4): 431-448. DOI: 10.1016/j.jksuci.2017.06.001.

BEAM A L, KOHANE I S. Big data and machine learning in health care[J]. JAMA, 2018, 319(13): 1317-1318. DOI: 10.1001/jama.2017.18391.

HASSANAT A B A. Furthest-pair-based binary search tree for speeding big data classification using K-nearest neighbors[J]. Big Data, 2018, 6(3): 225-235. DOI: 10.1089/big.2018.0064.

HU X G, ZHOU P, LI P P, et al. A survey on online feature selection with streaming features[J]. Frontiers of Computer Science, 2018, 12(3): 479-493. DOI: 10.1007/s11704-016-5489-3.

易明雨, 肖赤心, 潘晖, 等. 用于大数据分类的快速隐层优化分布式极限学习机[J]. 计算机工程与应用, 2019, 55(16): 165-169. DOI: 10.3778/j.issn.1002-8331.1804-0352. YI M Y, XIAO C X, PAN H, et al. Fast hidden layer optimal extreme learning machine for big data classification[J]. Computer Engineering and Applications, 2019, 55(16): 165-169. DOI: 10.3778/j.issn.1002-8331.1804-0352.

VARATHARAJAN R, MANOGARAN G, PRIYAN M K. A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing[J]. Multimedia Tools and Applications, 2018, 77(8): 10195-10215. DOI: 10.1007/s11042-017-5318-1.

KAUR D, AUJLA G S, KUMAR N, et al. Tensor-based big data management scheme for dimensionality reduction problem in smart grid systems: SDN perspective[J]. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(10): 1985-1998. DOI: 10.1109/TKDE.2018.2809747.

NURLAILY D, IRHAMAH, PURNAMI S W, et al. Support vector machine for imbalanced microarray dataset classification using ant colony optimization and genetic algorithm[J]. AIP Conference Proceedings, 2019, 2194(1): 020076. DOI: 10.1063/1.5139808.

ZHOU P, HU X G, LI P P, et al. Online feature selection for high-dimensional class-imbalanced data[J]. Knowledge-Based Systems, 2017(136): 187-199. DOI: 10.1016/j.knosys.2017.09.006.

LAKSHMANAPRABU S K, SHANKAR K, ILAYARAJA M, et al. Random forest for big data classification in the internet of things using optimal features[J]. International Journal of Machine Learning and Cybernetics, 2019, 10(10): 2609-2618. DOI: 10.1007/s13042-018-00916-z.

林倩瑜. 云服务环境下的大数据多标签属性分类技术[J]. 微电子学与计算机, 2019, 36(2): 101-104. DOI: 10.19304/j.cnki.issn1000-7180.2019.02.021. LIN Q Y. Big data multi-label attribute classification technology in cloud service environment[J]. Microelectronics % Computer, 2019, 36(2): 101-104. DOI: 10.19304/j.cnki.issn1000-7180.2019.02.021.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

一种处理不平衡大数据的并行随机森林算法

基于MapReduce和矩阵的频繁项集挖掘算法

用于多个MapReduce作业的任务调度算法

超低功耗实时时钟电路的设计

基于反向体偏置的高增益稳定性高温运放设计