The imputation of missing values is an important research content in incomplete data analysis. Based on the auto associative neural network (AANN), this paper conducts regression modeling for incomplete data and imputes missing values. Since the AANN can estimate missing values in multiple missingness patterns efficiently, we introduce incomplete records into the modeling process and propose an attribute cross fitting model (ACFM) based on AANN. ACFM reconstructs the path of data transmission between output and input neurons and optimizes the model parameters by training errors of existing data, thereby improving its own ability to fit relations between attributes of incomplete data. Besides, for the problem of incomplete model input, this paper proposes a model training scheme, which sets missing values as variables and makes missing value variables update with model parameters iteratively. The method of local learning and global approximation increases the precision of model fitting and the imputation accuracy of missing values. Finally, experiments based on several datasets verify the effectiveness of the proposed method.

中文翻译：

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属性相关的神经元建模和不完整数据的缺失值估算

缺失值的估算是不完整数据分析中的重要研究内容。基于自动联想神经网络（AANN），本文对不完整的数据进行了回归建模，并​​估算了缺失值。由于AANN可以有效地估计多个缺失模式中的缺失值，因此我们将不完整的记录引入到建模过程中，并基于AANN提出了属性交叉拟合模型（ACFM）。ACFM通过训练现有数据的误差来重建输出和输入神经元之间的数据传输路径，并优化模型参数，从而提高其自身拟合不完整数据属性之间关系的能力。此外，针对模型输入不完整的问题，提出了一种模型训练方案，它将缺失值设置为变量，并使缺失值变量通过模型参数迭代更新。局部学习和全局逼近的方法提高了模型拟合的精度和缺失值的插补精度。最后，基于几个数据集的实验证明了该方法的有效性。