| 基于人工鱼群算法的分类规则发现 |
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| 引用本文： | 陈俊清,朱文兴.基于人工鱼群算法的分类规则发现[J].福州大学学报(自然科学版),2007,35(1):25-30. |
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| 作者姓名： | 陈俊清 朱文兴 |
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| 作者单位： | 福州大学数学与计算机科学学院,福建,福州,350002 |
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| 摘 要： | 人工鱼群算法(AFSA)是一种最新提出的新型仿生优化算法,具有良好的克服局部极值和获得全局极值的能力.利用鱼群算法进行分类规则挖掘,建立了相应的优化模型.通过对公用数据的实验和CN2算法的对比表明,本算法可得到预测准确率较高的分类规则,同时规则更为简单.
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| 关 键 词： | 人工鱼群算法 分类规则 数据挖掘 |
| 文章编号： | 1000-2243(2007)01-0025-06 |
| 修稿时间： | 2006年4月24日 |
Classification rule discovering with artificial fish-swarm algorithm |
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| CHEN Jun-qing,ZHU Wen-xing.Classification rule discovering with artificial fish-swarm algorithm[J].Journal of Fuzhou University(Natural Science Edition),2007,35(1):25-30. |
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| Authors: | CHEN Jun-qing ZHU Wen-xing |
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| Institution: | (College of Mathematics and Computer Science,Fuzhou University,Fuzhou,Fujian 35002,China) |
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| Abstract: | Artificial fish-swarm algorithm(AFSA) is a nove1 bio-inspired optimizing method,which possesses good capability to avoid the local extremum and obtain the global extremum.Artificial fish-swarm algorithm is used to discover classification rules from a categorical database and a model based on this method is also presented for the first time here.Compared with CN2,a well-known data mining algorithm for classification,in two public domain data sets,the results provide evidence that,our method can discover simpler classification rules,including a rule set with better predictive accuracy rate. |
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| Keywords: | artificial fish-swarm algorithm classification rule data mining |
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