| 基于最优支持向量机模型的经营失败预警研究 |
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| 引用本文： | 宋新平,丁永生. 基于最优支持向量机模型的经营失败预警研究[J]. 管理科学, 2008, 21(1): 115-120 |
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| 作者姓名： | 宋新平 丁永生 |
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| 作者单位： | 东华大学旭日工商管理学院,上海,201620;江苏大学工商管理学院,江苏,镇江,212013;东华大学信息科学与技术学院,上海,201620 |
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| 基金项目： | 教育部哲学社会科学研究重大课题 , 教育部跨世纪优秀人才培养计划 , 上海市教育委员会科研创新项目 |
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| 摘 要： | 根据中国资本市场的实际和样本数据特点,设计一套从样本准备到模型参数优化、再到模型比较的集成解决方案,对上市公司经营失败进行预警,通过实验分析参数调整和核函数选择对支持向量机建模的影响,寻求最优的支持向量机模型.实证结果表明,经营失败预警应用中,参数和核函数的选择对预警模型有较大影响,基于最优支持向量机模型的预测效果优于统计方法和神经网络方法,支持向量机适合中国上市公司分行业小样本的实际.特别处理事件作为经营失败样本切分标准对模型产生一定影响.
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| 关 键 词： | 经营失败预警 参数与核函数 最优支持向量机模型 人工智能 |
| 文章编号： | 1672-0334(2008)01-0115-07 |
Study on Business Failure Prediction Based on an Optimized Support Vector Machine Model |
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| SONG Xin-ping,DING Yong-sheng. Study on Business Failure Prediction Based on an Optimized Support Vector Machine Model[J]. Journal of Management Science, 2008, 21(1): 115-120 |
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| Authors: | SONG Xin-ping DING Yong-sheng |
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| Affiliation: | SONG Xin-ping1,2,DING Yong-sheng31 Glorious Sun School of Business , Management,Donghua University,Shanghai 201620,China2 School of Business Administration,Jiangsu University,Zhenjiang 212013,China3 College of Information Sciences , Technology,China |
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| Abstract: | According to feature of Chinese capital market and samples,the paper designs an integrated scheme including sampling preparation,optimization of models and model comparison for business failure prediction of listed companies.The effects of parameter-adjusting and selection of kernel functions on model performance by simulation experiments is discussed.Then an optimized support vector machine model is build.Empirical results show that kernel function and parameters have effect on the performance of the suppo... |
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| Keywords: | business failure prediction model parameter and kernel function the optimized support vector machine model artificial intelligence |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
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