TITLE: Machine learning with incomplete datasets using multi-objective optimization models 

ABSTRACT: Machine learning techniques have been developed to learn from complete data.
When missing values exist in a dataset, the incomplete data should be
preprocessed separately by removing data points with missing values or
imputation. In this paper, we propose an online approach to handle missing
values while a classification model is learnt. To reach this goal, we develop a
multi-objective optimization model with two objective functions for imputation
and model selection. We also propose three formulations for imputation
objective function. We use an evolutionary algorithm based on NSGA II to find
the optimal solutions as the Pareto solutions. We investigate the reliability
and robustness of the proposed model using experiments by defining several
scenarios in dealing with missing values and classification. We also describe
how the proposed model can contribute to medical informatics. We compare the
performance of three different formulations via experimental results. The
proposed model results get validated by comparing with a comparable literature.
Machine learning techniques have been developed to learn from complete data.
When missing values exist in a dataset, the incomplete data should be
preprocessed separately by removing data points with missing values or
imputation. In this paper, we propose an online approach to handle missing
values while a classification model is learnt. To reach this goal, we develop a
multi-objective optimization model with two objective functions for imputation
and model selection. We also propose three formulations for imputation
objective function. We use an evolutionary algorithm based on NSGA II to find
the optimal solutions as the Pareto solutions. We investigate the reliability
and robustness of the proposed model using experiments by defining several
scenarios in dealing with missing values and classification. We also describe
how the proposed model can contribute to medical informatics. We compare the
performance of three different formulations via experimental results. The
proposed model results get validated by comparing with a comparable literature.
Machine learning with incomplete datasets using multi-objective optimization models
http://arxiv.org/abs/2012.13352v1