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一种改进的嵌入式特征选择算法及应用
A Novel Embedded Feature Selection Algorithm and Its Application
【摘要】 针对非线性多分类问题,提出了一个改进的嵌入最小-最大值特征选择算法,并与支持向量机算法结合,提出了针对复杂的组合优化问题的启发式算法。为验证方法的有效性,在钢板缺陷识别工程数据集上进行了实验,表明所提出的方法具有较高的求解速度和预测准确度。
【Abstract】 An improved embedded min-max feature selection algorithm was proposed for the nonlinear multilabel classification problem,and in combination with the support vector machine algorithm,a heuristic algorithm was proposed for the complex combinatorial optimization problem. The efficiency and accuracy of the proposed algorithm were verified after a series of experiments conducted on steel faults diagnosis dataset.
【关键词】 最小-最大值优化问题;
特征选择;
非线性多分类支持向量机;
【Key words】 min-max optimization; feature selection algorithm; nonlinear multi-label support vector machine;
【Key words】 min-max optimization; feature selection algorithm; nonlinear multi-label support vector machine;
- 【文献出处】 同济大学学报(自然科学版) ,Journal of Tongji University(Natural Science) , 编辑部邮箱 ,2022年02期
- 【分类号】TP18
- 【被引频次】5
- 【下载频次】357