| 一种大规模数据库的组合优化决策树算法 |
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| 引用本文： | 赵静娴,倪春鹏,詹原瑞,杜子平.一种大规模数据库的组合优化决策树算法[J].系统工程与电子技术,2009,31(3):583-587. |
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| 作者姓名： | 赵静娴 倪春鹏 詹原瑞 杜子平 |
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| 作者单位： | 1. 天津大学管理学院, 天津, 300072;2. 天津科技大学经管学院, 天津, 300222 |
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| 基金项目： | 国家自然科学基金，天津科技大学科学研究基金 |
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| 摘 要： | 提出了一种适合于大规模高维数据库的组合优化决策树算法。相比于传统的类似算法,该算法从数据的离散化,降维,属性选择三方面进行改进,对决策树建立过程中不适应大规模高维数据库的主要环节进行了优化,有效解决了处理大规模高维数据库问题的效率和精度之间的矛盾。仿真试验表明,该算法在大大减少了计算代价的同时提高了决策树的分类精度。
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| 关 键 词： | 离散化 降维 属性选择 决策树 数据挖掘 |
| 收稿时间： | 2007-10-22 |
| 修稿时间： | 2007-12-20 |
Combined optimization decision tree algorithm suitable for large scale data-base |
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| ZHAO Jing-xian,NI Chun-peng,ZHAN Yuan-rui,DU Zi-ping.Combined optimization decision tree algorithm suitable for large scale data-base[J].System Engineering and Electronics,2009,31(3):583-587. |
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| Authors: | ZHAO Jing-xian NI Chun-peng ZHAN Yuan-rui DU Zi-ping |
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| Institution: | 1. School of Management, Tianjin Univ., Tianjin 300072, China;2. School of Economics and Management, Tianjin Univ. of Science & Technology, Tianjin 300222, China |
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| Abstract: | A combined optimization decision tree algorithm suitable for a large scale and high dimension data-base is presented.Compared with the traditional similar algorithms,the algorithm makes improvements from three aspects: discretization,reducing dimension and attribute selection.It also optimizes the main processes,so that it is suitable for large scale and high dimension data-base and effectively solves the conflict between efficiency and predictive precision.Experiments show that the proposed method raises the predictive precision of decision trees while it greatly reduces the computational cost. |
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