# Multi-objective Feature Selection with Missing Data in Classification

arXiv: https://arxiv.org/abs/2104.08747 · HTML: https://mlsanity.com/paper/2104.08747/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 50 references that are in the map.
- First posted on arXiv 18 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization](https://mlsanity.com/paper/1701.00879/) (2017)
- [Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization](https://mlsanity.com/paper/1711.07907/) (2017)
- [IGD Indicator-based Evolutionary Algorithm for Many-objective Optimization Problems](https://mlsanity.com/paper/1802.08792/) (2018)

## Built on by (most cited first, 4 of 4)

- [Evolutionary approaches to explainable machine learning](https://mlsanity.com/paper/2306.14786/) (2023)
- [Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems](https://mlsanity.com/paper/2406.07811/) (2024)
- [Flexible categorization using formal concept analysis and Dempster-Shafer theory](https://mlsanity.com/paper/2408.15012/) (2024)
- [A data-science pipeline to enable the Interpretability of Many-Objective Feature Selection](https://mlsanity.com/paper/2311.18746/) (2023)

## Abstract (arXiv)

Feature selection (FS) is an important research topic in machine learning. Usually, FS is modelled as a+ bi-objective optimization problem whose objectives are: 1) classification accuracy; 2) number of features. One of the main issues in real-world applications is missing data. Databases with missing data are likely to be unreliable. Thus, FS performed on a data set missing some data is also unreliable. In order to directly control this issue plaguing the field, we propose in this study a novel modelling of FS: we include reliability as the third objective of the problem. In order to address the modified problem, we propose the application of the non-dominated sorting genetic algorithm-III (NSGA-III). We selected six incomplete data sets from the University of California Irvine (UCI) machine learning repository. We used the mean imputation method to deal with the missing data. In the experiments, k-nearest neighbors (K-NN) is used as the classifier to evaluate the feature subsets. Experimental results show that the proposed three-objective model coupled with NSGA-III efficiently addresses the FS problem for the six data sets included in this study.