# Modified Genetic Algorithm for Feature Selection and Hyper Parameter Optimization: Case of XGBoost in Spam Prediction

arXiv: https://arxiv.org/abs/2310.19845 · HTML: https://mlsanity.com/paper/2310.19845/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 85 references that are in the map.
- First posted on arXiv 30 Oct 2023.

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

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification](https://mlsanity.com/paper/2106.05841/) (2021)
- [A pipeline and comparative study of 12 machine learning models for text classification](https://mlsanity.com/paper/2204.06518/) (2022)

## Abstract (arXiv)

Recently, spam on online social networks has attracted attention in the research and business world. Twitter has become the preferred medium to spread spam content. Many research efforts attempted to encounter social networks spam. Twitter brought extra challenges represented by the feature space size, and imbalanced data distributions. Usually, the related research works focus on part of these main challenges or produce black-box models. In this paper, we propose a modified genetic algorithm for simultaneous dimensionality reduction and hyper parameter optimization over imbalanced datasets. The algorithm initialized an eXtreme Gradient Boosting classifier and reduced the features space of tweets dataset; to generate a spam prediction model. The model is validated using a 50 times repeated 10-fold stratified cross-validation, and analyzed using nonparametric statistical tests. The resulted prediction model attains on average 82.32% and 92.67% in terms of geometric mean and accuracy respectively, utilizing less than 10% of the total feature space. The empirical results show that the modified genetic algorithm outperforms Chi2 and PCA feature selection methods. In addition, eXtreme Gradient Boosting outperforms many machine learning algorithms, including BERT-based deep learning model, in spam prediction. Furthermore, the proposed approach is applied to SMS spam modeling and compared to related works.