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 $Chi^2$
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.

近期，网上社交网络的垃圾邮件引起了研究和商业界的关注。本文提出了一种改进的遗传算法用于同时减少维度和超参数优化，针对不平衡数据集。该算法使用极限梯度提升分类器对推文数据集进行特征空间降维，生成垃圾邮件预测模型，并验证了模型的有效性。实证结果表明，改进的遗传算法在特征选择方面优于 Chi^2 和 PCA 方法，而极限梯度提升在垃圾邮件预测方面优于包括基于 BERT 的深度学习模型在内的许多机器学习算法。此外，该方法还应用于短信垃圾邮件建模并与相关工作进行比较。

特征选择与超参数优化的改进遗传算法：垃圾邮件预测中的 XGBoost 案例

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

Many computer vision algorithms depend on a variety of parameter choices and
settings that are typically hand-tuned in the course of evaluating the
algorithm. While such parameter tuning is often presented as being incidental
to the algorithm, correctly setting these parameter choices is frequently
critical to evaluating a method's full potential. Compounding matters, these
parameters often must be re-tuned when the algorithm is applied to a new
problem domain, and the tuning process itself often depends on personal
experience and intuition in ways that are hard to describe. Since the
performance of a given technique depends on both the fundamental quality of the
algorithm and the details of its tuning, it can be difficult to determine
whether a given technique is genuinely better, or simply better tuned.
In this work, we propose a meta-modeling approach to support automated hyper
parameter optimization, with the goal of providing practical tools to replace
hand-tuning with a reproducible and unbiased optimization process. Our approach
is to expose the underlying expression graph of how a performance metric (e.g.
classification accuracy on validation examples) is computed from parameters
that govern not only how individual processing steps are applied, but even
which processing steps are included. A hyper parameter optimization algorithm
transforms this graph into a program for optimizing that performance metric.
Our approach yields state of the art results on three disparate computer vision
problems: a face-matching verification task (LFW), a face identification task
(PubFig83) and an object recognition task (CIFAR-10), using a single algorithm.
More broadly, we argue that the formalization of a meta-model supports more
objective, reproducible, and quantitative evaluation of computer vision
algorithms, and that it can serve as a valuable tool for guiding algorithm
development.

本研究提出了一种元模型方法，支持自动化超参数优化，以取代手动调试，实现易复制和公正的优化过程，并在三个不同的计算机视觉问题上获得了最新的结果。