Sparse multi-label feature selection via pseudo-label learning and dynamic graph constraints

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 102975 - 102975

Published: Jan. 1, 2025

Language: Английский

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Unsupervised feature selection using sparse manifold learning: Auto-encoder approach

Information Processing & Management, Journal Year: 2024, Volume and Issue: 62(1), P. 103923 - 103923

Published: Oct. 18, 2024

Language: Английский

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Exploring View-Specific Label Relationships for Multi-View Multi-Label Feature Selection

Information Sciences, Journal Year: 2024, Volume and Issue: 681, P. 121215 - 121215

Published: July 23, 2024

Language: Английский

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Graph diffusion with dual-distance metrics for missing multi-label feature selection

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: 271, P. 126662 - 126662

Published: Jan. 30, 2025

Language: Английский

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Granular Ball-based Partial Label Feature Selection via Fuzzy Correlation and Redundancy

Information Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 122047 - 122047

Published: March 1, 2025

Language: Английский

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Feature subset selection for big data via parallel chaotic binary differential evolution and feature-level elitism

Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 123, P. 110232 - 110232

Published: March 15, 2025

Language: Английский

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A machine learning-aided framework for hierarchical management of building structural safety

Engineering Construction & Architectural Management, Journal Year: 2025, Volume and Issue: unknown

Published: March 20, 2025

Purpose Insufficient attention to the building’s structural safety conditions has led loss of life and property as well disastrous social impacts. Although some countries or regions have developed building management policies, they seem lack a solid decision-making basis efficiency. To address this, this paper aims establish data-driven framework achieve economic, efficient accurate safety. Design/methodology/approach This proposes novel for hierarchical using machine learning approaches. A case study in Chongqing, China, is adopted demonstrate its application prove feasibility. The considers database, prediction safety, iteration. Findings results indicate effectiveness proposed framework, which facilitates an existing condition limited fundamental information, allowing design that encompasses structure, mechanisms measures. Furthermore, iteration introduced allow continuous improvement adaptation over time. Practical implications By introducing actions could be taken distinguished buildings, optimizing resource allocation enhancing engineering maintenance. also offers practical guidance decisions regarding new construction. Originality/value provides valuable insights research practice intelligent cost-effective buildings contributes urban renewal.

Language: Английский

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Multi-label feature selection via label relaxation

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 113047 - 113047

Published: March 1, 2025

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Screening for Anomalous Safety Condition Among Existing Buildings Using Explainable Machine Learning

Structural Control and Health Monitoring, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

To ensure a safe environment for occupants, evaluating the physical status and service performance of existing buildings is essential. However, large‐scale building condition assessment usually relies on expertise judgment inspectors, which can be costly laborious due to unclear priorities, ambiguous procedures, ineffective operations. address these challenges, this study proposes an explainable machine learning‐based screening model anomalous safety among buildings, narrowing down scope requiring further detailed inspection monitoring. Initially, imbalanced dataset 18,090 survey reports unsafe labels collected. Then, synthetic minority oversampling technique (SMOTE) conducted balance dataset. Subsequently, seven learning models are trained utilizing 10‐fold cross‐validation with grid search. Findings reveal that, based balanced dataset, ensemble significantly better than that individual models. Specifically, XGBoost achieves highest performance, macro‐F1 98.49%, G‐mean value accuracy 98.49%. The final predictive (the SMOTE‐based model) explained using SHapley Additive exPlanations (SHAP). Service year, structure, location three most important features influencing structural safety. This represents promising approach automated optimizing resource allocation, enhancing effectiveness in decision‐making construction maintenance.

Language: Английский

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Pointwise fuzzy mutual information based multi-label feature selection via feature low-rank regularization

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 113301 - 113301

Published: June 1, 2025

Language: Английский

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