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

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。

An improved embedded min-max feature selection algorithm was proposed for the nonlinear multi-label 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.