New fast feature selection methods based on multiple support vector data description

Zhang Li; Lu Xingning

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New fast feature selection methods based on multiple support vector data description

机译：基于多个支持向量数据描述的新快速特征选择方法

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摘要

Feature selection can sort out useful features to obtain good performance when dealing with high-dimensional data. Feature selection methods based on support vector data description (SVDD) have been proposed for one-class classification problems: SVDD-radius-recursive feature elimination (SVDD-RRFE) and SVDD-dual-objective-recursive feature elimination (SVDD-DRFE). However, both SVDD-RRFE and SVDD-DRFE use only one-class samples even given a multi-class classification task, and suffer from high computational complexity. To remedy it, this paper extends both SVDD-RRFE and SVDD-DRFE to binary and multi-class classification problems using multiple SVDD models, and proposes fast feature ranking schemes for them in the case of the linear kernel. Experimental results on toy, UCI and microarray datasets show the efficiency and the feasibility of the proposed methods.

机译：特征选择可以在处理高维数据时解决有用的功能以获得良好的性能。已经提出了基于支持向量数据描述（SVDD）的特征选择方法，用于单级分类问题：SVDD-RADIUS递归功能消除（SVDD-RRFE）和SVDD-Dual-Objective递归特征消除（SVDD-DRFE）。然而，SVDD-RRFE和SVDD-DRFE均仅使用一类样本，甚至给出多级分类任务，并且遭受高计算复杂性。要解决此问题，使用多个SVDD型号将SVDD-RRFE和SVDD-DRFE扩展到二进制和多级分类问题，并在线性内核的情况下为它们提供快速特征排序方案。玩具，UCI和微阵列数据集的实验结果显示了所提出的方法的效率和可行性。

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来源
《Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies》 |2018年第7期|共15页
作者
Zhang Li; Lu Xingning;
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作者单位

Soochow Univ Sch Comp Sci &

Technol Joint Int Res Lab Machine Learning &

Neuromorph C Suzhou 215006 Jiangsu Peoples R China;

Soochow Univ Sch Comp Sci &

Technol Joint Int Res Lab Machine Learning &

Neuromorph C Suzhou 215006 Jiangsu Peoples R China;

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原文格式 PDF
正文语种 eng
中图分类自动化技术、计算机技术;
关键词
Feature selection; Classification problems; Support vector data description; Multiple models;

机译：特征选择;分类问题;支持矢量数据描述;多种型号;

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专利

1. New fast feature selection methods based on multiple support vector data description [J] . Zhang Li, Lu Xingning Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies . 2018,第7期

机译：基于多个支持向量数据描述的新快速特征选择方法
2. Novel feature selection method based on Stochastic Methods Coupled to Support Vector Machines using H- NMR data (data of olive and hazelnut oils) [J] . Claudia Isaza, Cristhian Manuel Duran, Oscar Eduardo Gualdron Revista Ion . 2015,第2期

机译：使用H-NMR数据（橄榄油和榛子油的数据）结合支持向量机的基于随机方法的新特征选择方法
3. Fabric Defect Detection Based On Multiple Fractal Features And Support Vector Data Description [J] . Hong-gang Bu, Jun Wang, Xiu-bao Huang Engineering Applications of Artificial Intelligence . 2009,第2期

机译：基于多个分形特征和支持向量数据描述的织物疵点检测
4. Feature selection for multi-class classification using support vector data description [C] . 36th Annual Conference on IEEE Industrial Electronics Society . 2010

机译：使用支持向量数据描述进行多类别分类的特征选择
5. A new feature selection method based on support vector machines for text categorization. [D] . Xu, Yaquan. 2006

机译：一种基于支持向量机的文本分类新特征选择方法。
6. An Efficient Feature Selection Strategy Based on Multiple Support Vector Machine Technology with Gene Expression Data [O] . Ying Zhang, Qingchun Deng, Wenbin Liang, -1

机译：基于多支持向量机技术的基因表达数据有效特征选择策略
7. A fast gene selection method for multi-cancer classification using multiple support vector data description [O] . Cao Jin, Zhang Li, Wang Bangjun, 2015

机译：基于多支持向量数据描述的多基因分类快速基因选择方法

New fast feature selection methods based on multiple support vector data description

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