Classifying patterns with missing values using Multi-Task Learning perceptrons

Datasets with missing values are frequent in real-world classification problems. It seems obvious that imputation of missing values can be considered as a series of secondary tasks, while classification is the main purpose of any machine dealing with these datasets. Consequently, Multi-Task Learning (MTL) schemes offer an interesting alternative approach to solve missing data problems. In this paper, we propose an MTL-based method for training and operating a modified Multi-Layer Perceptron (MLP) architecture to work in incomplete data contexts. The proposed approach achieves a balance between both classification and imputation by exploiting the advantages of MTL. Extensive experimental comparisons with well-known imputation algorithms show that this approach provides excellent results. The method is never worse than the traditional algorithms – an important robustness property – and, also, it clearly outperforms them in several problems.

Highlight
► This paper provides a reasonable classification-oriented imputation mechanism.
► Multi-Task Learning offers an interesting approach to solve missing data problems.
► Classification and imputation are combined in only one neural architecture.
► A balance between both classification and imputation tasks is achieved using MTL.
► Extensive experiments show the proposed method has a valuable robustness property.