Abstract
This study conducts an empirical examination of MLP networks investigated through a rigorous methodical experimentation process involving three diverse datasets: TinyFace, Heart Disease, and Iris. Study Overview: The study includes three key methods: a) a baseline training using the default settings for the Multi-Layer Perceptron (MLP), b) feature selection using Genetic Algorithm (GA) based refinement c) Principal Component Analysis (PCA) based dimension reduction. The results show important information on how such techniques affect performance. While PCA had showed benefits in low-dimensional and noise-free datasets GA consistently increased accuracy in complex datasets by accurately identifying critical features. Comparison reveals that feature selection and dimensionality reduction play interdependent roles in enhancing MLP performance. The study contributes to the literature on feature engineering and neural network parameter optimization, offering practical guidelines for a wide range of machine learning tasks
Abstract (translated)
这项研究通过严格的系统实验过程对多层感知机（MLP）网络进行了实证考察，涉及三个不同的数据集：TinyFace、心脏病和鸢尾花。**研究概览**：该研究包括三种关键方法： a) 使用默认设置进行基线训练的Multi-Layer Perceptron (MLP) b) 采用遗传算法（GA）优化后的特征选择 c) 基于主成分分析（PCA）的降维 结果展示了这些技术如何影响性能的重要信息。在低维度和噪声较少的数据集中，PCA显示出其益处；而在复杂数据集上，基于GA的精确识别关键特征则持续提高了准确率。比较显示，特征选择与降维在增强MLP性能方面扮演着相互依赖的角色。 该研究为特征工程及神经网络参数优化领域的文献做出了贡献，并提供了适用于广泛机器学习任务的实际指导方针。
URL
https://arxiv.org/abs/2506.10184
