Method to Design Pattern Classification Model with Block Missing Training Data

Abstract

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.