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Machine learning techniques have been developed to learn from complete data.When missing values exist in a dataset, the incomplete data should bepreprocessed separately by removing data points with missing values orimputation. In this paper, we propose an online approach to handle missingvalues while a classification model is learnt. To reach this goal, we develop amulti-objective optimization model with two objective functions for imputationand model selection. We also propose three formulations for imputationobjective function. We use an evolutionary algorithm based on NSGA II to findthe optimal solutions as the Pareto solutions. We investigate the reliabilityand robustness of the proposed model using experiments by defining severalscenarios in dealing with missing values and classification. We also describehow the proposed model can contribute to medical informatics. We compare theperformance of three different formulations via experimental results. Theproposed model results get validated by comparing with a comparable literature.

Machine learning with incomplete datasets using multi-objective optimization models