| 因子分析降维对分类性能的影响研究 |
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| 引用本文： | 石洪波,吕亚丽,SHI Hong-bo,Lü Ya-li.因子分析降维对分类性能的影响研究[J].中北大学学报,2007,28(6). |
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| 作者姓名： | 石洪波 吕亚丽 SHI Hong-bo Lü Ya-li |
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| 作者单位： | 山西财经大学信息管理学院 山西太原030006 |
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| 基金项目： | 国家自然科学基金
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山西省自然科学基金 |
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| 摘 要： | 考虑因子数据的数据特征,采用连续属性服从正态分布的朴素贝叶斯分类方法,对因子分析降维前后数据集的分类性能变化进行了研究.实验结果表明:因子分析中的KMO(Kaiser-Meyer-Olkin)统计值和变量共同度与分类性能紧密相关,当KMO统计值大于0.8,并且只有很少属性的变量共同度值小于80%时,采用因子分析作为分类之前的降维是适宜的.
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| 关 键 词： | 因子分析 分类 朴素贝叶斯 降维 |
Research on the Effect of Factor-Analysis-Based Dimension Reduction on Classification Performance |
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| SHI Hong-bo.Research on the Effect of Factor-Analysis-Based Dimension Reduction on Classification Performance[J].Journal of North University of China,2007,28(6). |
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| Authors: | SHI Hong-bo |
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| Abstract: | Considering the inherent feature of factor data,the Naive Bayes classifier,which makes the assumption that numeric attributes are generated by a single Normal distribution,is adopted.The classification performance of factor data sets is studied both before and after dimension reduction.Experimental results show that the statistic value of Kaiser-Meyer-Olkin(KMO) and the communalities of factor analysis are related with classification accuracy.When the value of KMO is larger than 0.8 and the little part of communalities are smaller than 80%,it is appropriated for the classification to use factor analysis as dimension reduction method. |
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| Keywords: | factor analysis classification Naive Bayes dimension reduction |
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