摘要翻译：
提出了一种基于决策树的自联想神经网络(AANN)模型和基于主成分分析神经网络（PCA-NN）模型相结合的缺失数据归算方法。对于每个模型，决策树用于预测遗传算法的搜索范围，该遗传算法最小化从相应模型导出的误差函数。使用HIV血清流行数据测试和比较了模型对缺失数据的估算能力。结果表明，AANN模型的平均准确率从75.8%提高到86.3%，而PCA-NN模型的平均准确率从66.1%提高到81.6%，平均准确率提高了13%。
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英文标题：
《Estimation of Missing Data Using Computational Intelligence and Decision
  Trees》
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作者：
George Ssali and Tshilidzi Marwala
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最新提交年份：
2007
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分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  This paper introduces a novel paradigm to impute missing data that combines a decision tree with an auto-associative neural network (AANN) based model and a principal component analysis-neural network (PCA-NN) based model. For each model, the decision tree is used to predict search bounds for a genetic algorithm that minimize an error function derived from the respective model. The models' ability to impute missing data is tested and compared using HIV sero-prevalance data. Results indicate an average increase in accuracy of 13% with the AANN based model's average accuracy increasing from 75.8% to 86.3% while that of the PCA-NN based model increasing from 66.1% to 81.6%.
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PDF链接：
https://arxiv.org/pdf/709.164