标题： 数据填补技术的比较及其影响 显示英文标题

标题： Comparison of Data Imputation Techniques and their Impact

摘要： 调查或数据库中的缺失和不完整信息可以使用不同的统计和软计算技术进行插补。 本文全面比较了自关联神经网络（NN）、神经模糊（NF）系统以及上述方法与热deck插补的混合组合。 测试是在八类产前调查以及主成分分析（PCA）条件下进行的。 对于所有测试，神经网络的性能平均比神经模糊系统高出5.8%，而混合方法的准确性平均比单独使用NN或NF系统高出15.9%，但计算效率降低了50%。 通过多种统计检验对插补数据的全局影响进行了评估。 发现尽管插补的准确性很高，但插补数据的全局效应导致数据集之间的PCA关系发生变化。 插补数据集的标准差平均比实际数据集低36.7%，这可能导致结果的错误解释。 显示英文摘要

摘要： Missing and incomplete information in surveys or databases can be imputed using different statistical and soft-computing techniques. This paper comprehensively compares auto-associative neural networks (NN), neuro-fuzzy (NF) systems and the hybrid combinations the above methods with hot-deck imputation. The tests are conducted on an eight category antenatal survey and also under principal component analysis (PCA) conditions. The neural network outperforms the neuro-fuzzy system for all tests by an average of 5.8%, while the hybrid method is on average 15.9% more accurate yet 50% less computationally efficient than the NN or NF systems acting alone. The global impact assessment of the imputed data is performed by several statistical tests. It is found that although the imputed accuracy is high, the global effect of the imputed data causes the PCA inter-relationships between the dataset to become altered. The standard deviation of the imputed dataset is on average 36.7% lower than the actual dataset which may cause an incorrect interpretation of the results.