将微粒群和支持向量机用于耕地驱动因子选择的研究
Feature Subset Selection for Driving Forces of Cultivated Land Based on PSO-SVM
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摘要: 结合微粒群算法(PSO)具有执行速度快、受问题维数变化影响小的优点及支持向量机算法(SVM)结构风险最小化原理,构建了基于离散二进制微粒群(BPSO)与支持向量机的耕地驱动力因子选择方法,使用特征子集中确定的特征来训练支持向量回归机,用适应度函数来评价回归机的性能,指导BPSO的搜索。实验表明,该方法能有效地提取出耕地驱动因子的特征子集,从而降低了指标的维数,保留了关键信息,以获得知识的最小表达。Abstract: The particle swarm optimization(PSO) has the character of the high implementing speed.It is little affected by dimension.The support vector machines(SVM) is based on the theory of structural risk minimization.Combining the binary particle swarm optimization(BPSO) and the SVM,the feature subset selection for driving forces of cultivated land is constructed.This method uses the features defined in the feature subset to train the SVM which is evaluated by fitness fuction.The result of fitness fuction is utilized to direct the BPSO to search.The case study shows that this method has the excellent efficiency to select the feature subset.
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