用遗传算法优化MLP，提升小人脸识别准确率
Optimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace Recognition
- 用遗传算法筛选关键特征，替代传统降维
- 在复杂数据集上准确率提升，优于PCA方法
- 适合需要特征优化的中小规模人脸识别任务
本研究通过严谨的实验设计，在TinyFace、Heart Disease和Iris三个数据集上评估了多层感知机（MLP）的表现。实验包括三种方法：a) 使用默认参数训练MLP；b) 基于遗传算法（GA）的特征选择；c) 基于主成分分析（PCA）的降维。结果表明，在低维且无噪声的数据集上PCA表现良好，但在复杂数据集中，遗传算法持续提升了分类准确率，有效识别出关键特征。对比显示，特征选择与降维在提升MLP性能中具有相互依赖的作用。研究为特征工程与神经网络参数优化提供了实践指导，适用于多种机器学习任务。
原文摘要 · Abstract (English)
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
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