基于萤火虫群优化算法的烟草香级集成分类方法

发布时间：2018-02-12 23:26

  本文关键词： 选择性集成学习 萤火虫群优化算法 混合核SVM 烟草香级分类　出处：《数学的实践与认识》2017年20期 　论文类型：期刊论文

【摘要】：针对烟草化学成分与卷烟制品香级之间确定的数学模型难以建立的问题.提出了一种基于萤火虫群优化算法的烟草香级集成分类方法.方法首先使用混合核SVM独立训练多个个体支持向量机,然后利用改进的离散型萤火虫群优化算法选择部分精度较高、差异度较大的个体分类器参与集成,最后通过多数投票法得到最终的分类预测结果.对比实验结果表明,算法在分类准确度上具有较大的优势,证明了算法的有效性·从而为烟草的香级分类提供了可靠依据.
[Abstract]:In order to solve the problem that it is difficult to establish a mathematical model between the chemical composition of tobacco and the aroma grade of cigarette products, an integrated classification method of tobacco aroma grade based on firefly swarm optimization algorithm is proposed. The hybrid kernel SVM is used to separate the tobacco aroma grade. Stand training multiple individual support vector machines, Then the improved discrete firefly swarm optimization algorithm is used to select individual classifiers with higher accuracy and greater difference to participate in the ensemble. Finally, the final classification prediction results are obtained by majority voting. The algorithm has a great advantage in classification accuracy, which proves the validity of the algorithm and provides a reliable basis for tobacco aroma classification.
【作者单位】： 湖南环境生物职业技术学院生态宜居学院;中南林业科技大学计算机与信息工程学院;
【基金】：湖南省自然科学基金项目(10JJ3066)
【分类号】：TP18;TS41
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本文编号：1506804