| 预测型关联规则演化学习的适应值函数 |
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| 引用本文： | 许孝元,韩国强,闵华清. 预测型关联规则演化学习的适应值函数[J]. 华南理工大学学报(自然科学版), 2005, 33(5): 1-6 |
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| 作者姓名： | 许孝元 韩国强 闵华清 |
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| 作者单位： | 华南理工大学,计算机科学与工程学院,广东,广州,510640;华南理工大学,计算机科学与工程学院,广东,广州,510640;华南理工大学,计算机科学与工程学院,广东,广州,510640 |
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| 基金项目： | 广东省自然科学基金资助项目(31340)，广东省“千百十工程”优秀人才基金资助项目(Q02052)，广东省科技攻关项目(2003C101007)，广州市科技计划项目(2004J1-C008) |
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| 摘 要： | 为了提高基于遗传算法的分类预测准确度，探讨了评价规则质量的适应值函数，提出了基于置信度和支持度加权和的适应值函数，以取代传统的基于灵敏性和选择性的适应值函数．理论分析和实验结果都表明，文中提出的新适应值函数对于预测型关联规则演化搜索的引导作用明显地优于传统的适应值函数．新的适应值函数有利于改进基于遗传算法的机器学习．
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| 关 键 词： | 机器学习 演化学习 遗传算法 关联规则 分类 预测 |
| 文章编号： | 1000-565X(2005)05-0001-06 |
| 修稿时间： | 2004-07-05 |
Fitness Function for Evolutionary Learning of Predictive Association Rules |
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| XU Xiao-yuan,HAN Guo-Qiang,MIN Hua-qing. Fitness Function for Evolutionary Learning of Predictive Association Rules[J]. Journal of South China University of Technology(Natural Science Edition), 2005, 33(5): 1-6 |
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| Authors: | XU Xiao-yuan HAN Guo-Qiang MIN Hua-qing |
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| Abstract: | In order to improve the predicted accuracy of the classification based on genetic algorithm, the fitness functions for evaluating the rule equality are discussed in this paper. A fitness function based on a weighted sum of confidence and support is then proposed, which can substitutes the traditional fitness function based on sensitivity and specificity. Both the theoretically analytical and the experimental results show that the proposed fitness function has a greater advantage in the evolutionary search of predictive association rules over the traditional one, and is helpful to the improvement of the machine learning based on genetic algorithm. |
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| Keywords: | machine leaning evolutionary learning genetic algorithm association rule classification prediction |
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