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New MOEA-SSL method optimizes smart city classification with less data

By PulseAugur Editorial · [2 sources] ·

Researchers have developed MOEA-SSL, a novel multi-objective evolutionary approach for semi-supervised learning in smart city classification tasks. This method aims to optimize both predictive accuracy and feature usage, particularly in scenarios with limited labeled data. Experiments on smart city datasets demonstrated that MOEA-SSL can achieve competitive performance while significantly reducing the number of features required compared to existing methods like EA-SSL. AI

IMPACT This research could lead to more efficient AI models for smart city applications, reducing computational costs and data requirements.

RANK_REASON The cluster contains an academic paper detailing a new machine learning method.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New MOEA-SSL method optimizes smart city classification with less data

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The cluster contains an academic paper detailing a new machine learning method.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Francisco Jos\'e Sede\~no, Jamal Toutouh ·

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

    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…

  2. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Jamal Toutouh ·

    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…

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