一种改进的数据挖掘模糊支持向量机分类算法

发布时间：2018-02-01 14:36

本文关键词： 数据挖掘分类算法模糊支持向量机粒子群优化　出处：《兰州理工大学学报》2017年05期 　论文类型：期刊论文

【摘要】：针对模糊支持向量机(FSVM)应用于数据挖掘分类中存在对大样本集训练速度及分类速度慢的缺点,提出一种改进的数据挖掘模糊支持向量机分类算法.该算法首先预选有效的候选支持向量缩减训练样本集的规模提高训练速度;其次提出一种新的模糊隶属度函数,增强支持向量对构建模糊支持向量机最优分类超平面的作用,并用经过预选的训练样本集进行训练FSVM得到支持向量集;最后,运用粒子群优化算法选择最优支持向量子集,使用平均分类误差作为适应度函数,最终粒子输出时,将样本隶属度与设定阈值相比较,选择出支持向量集中相对较大隶属度的样本作为新的支持向量,提高分类速度.实验结果表明,该算法在不损失分类精度的情况下,提高了模糊支持向量机的训练速度和分类速度.
[Abstract]:For fuzzy support vector machine (FSVM) application in data mining classification there are shortcomings of slow training speed and classification speed for large sample set. An improved fuzzy support vector machine (FSVM) classification algorithm for data mining is proposed. Firstly, an effective candidate support vector reduction training sample set is pre-selected to improve the training speed. Secondly, a new fuzzy membership function is proposed to enhance the function of support vector to construct the optimal classification hyperplane of fuzzy support vector machine. The support vector set is obtained by training FSVM with pre-selected training sample set. Finally, the particle swarm optimization algorithm is used to select the optimal support vector subset, and the average classification error is used as the fitness function. When the final particle output, the sample membership degree is compared with the set threshold. In order to improve the classification speed, the new support vector with relatively large membership degree in the support vector set is selected. The experimental results show that the algorithm does not lose the classification accuracy. The training speed and classification speed of fuzzy support vector machine are improved.
【作者单位】：兰州理工大学电气工程与信息工程学院;
【基金】：国家自然科学基金(51265032)
【分类号】：TP18;TP311.13
【正文快照】： 数据挖掘中,分类是一个重要的研究方向,它可以描述数据的类模型,也可以预测未来的趋势.分类的目的是根据数据集的特点构造一个分类函数或分类模型,该模型可以将未知类别的样本映射到给定类别的某一种.现有的分类算法主要有决策树方法、支持向量机方法(SVM)、k最近邻方法、基于

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