Machine learning with incomplete datasets using multi-objective optimization models

TL;DR

This paper introduces an online multi-objective optimization approach using evolutionary algorithms to handle missing data during classification, improving robustness and applicability in fields like medical informatics.

Contribution

It presents a novel multi-objective optimization model with three formulations for imputation and model selection, integrated into an online learning framework.

Findings

The proposed model effectively handles missing data in classification tasks.

Experimental results show the superiority of the new formulations over existing methods.

The approach demonstrates robustness across various missing data scenarios.

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.