Classifying patterns with missing values using multi-task learning perceptrons Articles uri icon

publication date

  • March 2013

start page

  • 1333

end page

  • 1341

issue

  • 4

volume

  • 40

International Standard Serial Number (ISSN)

  • 0957-4174

Electronic International Standard Serial Number (EISSN)

  • 1873-6793

abstract

  • 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.

keywords

  • pattern classification; missing values; imputation; multi-task learning; multi-layer perceptron