# Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification

arXiv: https://arxiv.org/abs/2106.05841 · HTML: https://mlsanity.com/paper/2106.05841/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 62 references that are in the map.
- First posted on arXiv 30 May 2021.

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

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Feature Selection: A Data Perspective](https://mlsanity.com/paper/1601.07996/) (2016)
- [Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Mining](https://mlsanity.com/paper/1711.08477/) (2017)

## Built on by (most cited first, 6 of 6)

- [Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review](https://mlsanity.com/paper/2301.12222/) (2023)
- [Gene selection from microarray expression data: A Multi-objective PSO with adaptive K-nearest neighborhood](https://mlsanity.com/paper/2205.15020/) (2022)
- [Modified Genetic Algorithm for Feature Selection and Hyper Parameter Optimization: Case of XGBoost in Spam Prediction](https://mlsanity.com/paper/2310.19845/) (2023)
- [A Bio-Medical Snake Optimizer System Driven by Logarithmic Surviving Global Search for Optimizing Feature Selection and its application for Disorder Recognition](https://mlsanity.com/paper/2404.07216/) (2024)
- [Provably Robust Pre-Trained Ensembles for Biomarker-Based Cancer Classification](https://mlsanity.com/paper/2406.10087/) (2024)
- [ATwo-Stage Ensemble Feature Selection and Particle Swarm Optimization Approach for Micro-Array Data Classification in Distributed Computing Environments](https://mlsanity.com/paper/2507.04251/) (2025)

## Abstract (arXiv)

Microarray gene expression data are often accompanied by a large number of genes and a small number of samples. However, only a few of these genes are relevant to cancer, resulting in signigicant gene selection challenges. Hence, we propose a two-stage gene selection approach by combining extreme gradient boosting (XGBoost) and a multi-objective optimization genetic algorithm (XGBoost-MOGA) for cancer classification in microarray datasets. In the first stage, the genes are ranked use an ensemble-based feature selection using XGBoost. This stage can effectively remove irrelevant genes and yield a group comprising the most relevant genes related to the class. In the second stage, XGBoost-MOGA searches for an optimal gene subset based on the most relevant genes's group using a multi-objective optimization genetic algorithm. We performed comprehensive experiments to compare XGBoost-MOGA with other state-of-the-art feature selection methods using two well-known learning classifiers on 13 publicly available microarray expression datasets. The experimental results show that XGBoost-MOGA yields significantly better results than previous state-of-the-art algorithms in terms of various evaluation criteria, such as accuracy, F-score, precision, and recall.