English(EN) Multi-Objective Evolutionary Semi-Supervised Learning for Resource-Aware Smart City Classification

新的MOEA-SSL方法以更少的数据优化智慧城市分类

作者 PulseAugur 编辑部 · [2 个来源] · 2026-10-20 06:21

研究人员开发了MOEA-SSL，一种用于智慧城市分类任务的半监督学习的新型多目标进化方法。该方法旨在优化预测准确性和特征使用，特别是在标记数据有限的情况下。在智慧城市数据集上的实验表明，与EA-SSL等现有方法相比，MOEA-SSL可以在显著减少所需特征数量的同时实现具有竞争力的性能。 AI

影响这项研究可能带来更高效的智慧城市应用AI模型，降低计算成本和数据需求。

排序理由该集群包含一篇详细介绍新机器学习方法的学术论文。

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arXiv cs.LG TIER_1 English(EN) · Francisco Jos\'e Sede\~no, Jamal Toutouh · 2026-10-21 04:00

面向资源感知的智慧城市分类的多目标进化半监督学习

arXiv:2610.27703v1 Announce Type: new Abstract: Smart-city applications often combine scarce labeled data with abundant unlabeled observations. Semi-supervised learning (SSL) exploits both labeled and unlabeled data to improve classification under limited supervision. This articl…
arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Jamal Toutouh · 2026-10-20 06:21

Multi-Objective Evolutionary Semi-Supervised Learning for Resource-Aware Smart City Classification

Smart-city applications often combine scarce labeled data with abundant unlabeled observations. Semi-supervised learning (SSL) exploits both labeled and unlabeled data to improve classification under limited supervision. This article presents MOEA-SSL, a multi-objective evolution…