基于SA-SVM的中文文本分类研究
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摘要
基于支持向量机SVM的中文文本分类方法的泛化能力与其参数选取紧密相关,参数优化对文本分类精度有较大影响。为解决优化SVM参数难题,提出一种基于模拟退火(SA)优化SVM的文本分类方法。将文本分类准确率作为模拟退火的优化目标,利用SA良好的寻优能力搜索SVM的最优参数组合。在相同的数据集上进行实验,结果表明模拟退火具有稳定的全局搜索性能,是优化SVM参数的一种有效方式。相比其他文本分类算法,基于SA-SVM的中文文本分类的分类准确率更高,泛化能力更强,具有良好的分类性能。
The generalization ability of Chinese text categorization method based on SVM is closely related to its parameter selection, and parameter optimization has a great impact on the accuracy of text categorization. To solve the problem of optimizing SVM parameters, we proposed a text categorization method based on simulated annealing(SA). The accuracy of text categorization was taken as the optimization objective of SA, and the optimal parameter combination of SVM was searched by SA's good optimization ability. Experiments on the same data set show that SA has stable global search performance and is an effective way to optimize the parameters of SVM. Compared with other text categorization algorithms, the Chinese text categorization method based on SA-SVM has higher classification accuracy, stronger generalization ability and better classification performance.
The generalization ability of Chinese text categorization method based on SVM is closely related to its parameter selection, and parameter optimization has a great impact on the accuracy of text categorization. To solve the problem of optimizing SVM parameters, we proposed a text categorization method based on simulated annealing(SA). The accuracy of text categorization was taken as the optimization objective of SA, and the optimal parameter combination of SVM was searched by SA's good optimization ability. Experiments on the same data set show that SA has stable global search performance and is an effective way to optimize the parameters of SVM. Compared with other text categorization algorithms, the Chinese text categorization method based on SA-SVM has higher classification accuracy, stronger generalization ability and better classification performance.
引文
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[2] 钟将,刘荣辉. 一种改进的KNN文本分类[J]. 计算机工程与应用,2012,48(2):142-144.
[3] Shathi S P,Hossain M D,Nadim M,et al. Enhancing Performance of na?ve bayes in text classification by introducing an extra weight using less number of training examples[C]//2016 International Workshop on Computational Intelligence(IWCI). IEEE,2017:142-147.
[4] Bahassine S,Madani A,Kissi M. An improved Chi-sqaure feature selection for Arabic text classification using decision tree[C]//International Conference on Intelligent Systems: Theories and Applications. IEEE,2016.
[5] Goudjil M,Koudil M,Bedda M,et al. A novel active learning method using SVM for text classification[J]. International Journal of Automation and Computing,2018(3):1-9.
[6] 陈健飞, 蒋刚, 杨剑锋. 改进ABC-SVM的参数优化及应用[J]. 机械设计与制造, 2016(1):24-28.
[7] 张进,丁胜,李波. 改进的基于粒子群优化的支持向量机特征选择和参数联合优化算法[J]. 计算机应用,2016,36(5): 1330-1335.
[8] 庄严,白振林,许云峰. 基于蚁群算法的支持向量机参数选择方法研究[J]. 计算机仿真,2011,28(5):216-219.
[9] 陈晋音,熊晖,郑海斌. 基于粒子群算法的支持向量机的参数优化[J]. 计算机科学,2018,45(6):197-203.
[10] 王克奇,杨少春,戴天虹,等. 采用遗传算法优化最小二乘支持向量机参数的方法[J].计算机应用与软件,2009,26(7):109-111.
[11] 王万良,陈超,李笠,等. 基于模拟退火的自适应水波优化算法[J]. 计算机科学,2017,44(10):216-221.
[12] 司守奎,孙玺菁. 数学建模算法与应用[M]. 北京:国防工业出版社,2015.