Missing data is a usual drawback in many real-world applications of pattern classification. Methods of pattern classification with missing data are grouped into four types: (a) deletion of incomplete samples and classifier design using only the complete data portion, (b) imputation of missing data and learning of the classifier using the edited set, (c) use of model-based procedures and (d) use of machine learning procedures. These methods can be useful in case of small amount of missing values, but they may be unsuitable in case of relatively large amount of missing values. We proposed a method to design pattern classification model with block missing training data. First, we separated submatrices from the block missing training data. Second, we designed classification submodels using each submatrix. Third, we designed final classification model using a linear combination of these submodels. We tested the classifying accuracy rate and data usage rate of the classification model designed by means of the proposed method by simulation experiments on some datasets, and verified that the proposed method was effective from the viewpoint of classifying accuracy rate and data usage rate.

中文翻译：

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带块缺失训练数据的模式分类模型设计方法

在模式分类的许多实际应用中，缺失数据是一个常见的缺点。缺失数据的模式分类方法分为四种类型：（a）不完整样本的删除和仅使用完整数据部分的分类器设计，（b）缺失数据的插补和使用编辑集的分类器学习，（c）使用基于模型的程序和 (d) 使用机器学习程序。这些方法在缺失值较少的情况下很有用，但在缺失值相对较多的情况下可能不适用。我们提出了一种设计带有块缺失训练数据的模式分类模型的方法。首先，我们将子矩阵从块缺失的训练数据中分离出来。其次，我们使用每个子矩阵设计了分类子模型。第三，我们使用这些子模型的线性组合设计了最终的分类模型。通过在部分数据集上进行仿真实验，对利用本文方法设计的分类模型的分类准确率和数据使用率进行了测试，从分类准确率和数据使用率的角度验证了本文方法的有效性。