| 基于迭代稀疏组套索及SVM的高维分类研究 |
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| 引用本文：潘 雪 航 .基于迭代稀疏组套索及SVM的高维分类研究[J].计算技术与自动化,2021,(4):108-112 |
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| 中文摘要:高维数据存在大量的冗余变量和噪声，传统的分类方法在高维情况下通常效果不佳。为提高分类性能，将迭代稀疏组套索和支持向量机结合，提出了一种新的高维分类方法iSGL-SVM。分别在prostate和Tox_171数据集上验证了所提出的方法，并与其它三种方法进行比较。实验结果表明，该方法具有更好的变量选择效果和较高的分类精度，可广泛应用于高维小样本数据集的分类。 |
| 中文关键词:迭代稀疏组套索 支持向量机 高维分类 变量选择 |
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| High-dimensional Classification Based on Iterative Sparse Group Lasso and SVM |
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| Abstract:There are a lot of redundant variables and noise in high-dimensional data, and traditional classification methods usually do not work well in high-dimensional situations. In order to improve the classification performance, the iterative sparse group lasso is combined with support vector machine, and a new high-dimensional classification method iSGL-SVM is proposed. The proposed method was verified on the prostate and Tox_171 datasets respectivelyand compared with the other three methods. The experimental results showed that the method has better variable selection effects and higher classification accuracy, which can be widely used for classification of high-dimensional small sample datasets. |
| keywords:iterative sparse group lasso support vector machine high-dimensional classification variable selection |
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