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Efficient Utilization of Missing Data in Cost-Sensitive Learning

机译：高效利用缺失数据在成本敏感的学习中

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摘要

Different from previous imputation methods which impute missing values in the incomplete samples by using the information in the complete samples, this paper proposes a Date-drive Incremental imputation Model, DIM for short, which uses all available information in the data set to impute missing values economically, effectively, orderly, and iteratively. To this end, we propose a scoring rule to rank the missing features by taking into account both the economical criterion and the effective imputation information. The economical criterion takes both the imputation cost and the discriminative ability of the feature into account, while the effective imputation information enables to use all observed information in the data set including the imputed missing values to impute the left missing values. During the imputation process, our DIM first detects the neednot-impute samples for reducing the imputation cost and noise, and then selects the missing features with the top rank to impute first. The imputation process orderly imputes the missing features until all missing values are imputed or the imputation cost is exhausted. Experimental results on UCI data sets demonstrated the advantages of our proposed DIM, compared to the comparison methods, in terms of prediction accuracy and classification accuracy.
机译：本文提出了一种不同的估算方法，这些方法通过完整样本中的信息施加不完整的样本中的缺失值，提出了一个日期驱动增量归纳模型，短暂的暗淡，它使用数据集中的所有可用信息赋予缺失值经济，有效，有序，迭代。为此，我们提出了一个评分规则，通过考虑经济标准和有效的归因信息来对缺失的功能进行排名。经济标准考虑了特征的归纳成本和辨别能力，而有效的归纳信息使得能够在数据集中使用包括所缺失的缺失值的数据集中的所有观察到的信息来赋予左缺失值。在归纳过程中，我们的Dim首先检测到用于降低归属成本和噪声的舒适样本，然后选择顶级丢失的功能以赋予第一级。归纳过程有序耗尽缺失的功能，直到避免所有缺失值或归咎成本耗尽。与比较方法在预测准确性和分类准确性方面，UCI数据集的实验结果表明了我们所提出的暗淡的优点。

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