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

Information Fusion, Год журнала: 2025, Номер unknown, С. 102975 - 102975

Опубликована: Янв. 1, 2025

Язык: Английский

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

Information Processing & Management, Год журнала: 2024, Номер 62(1), С. 103923 - 103923

Опубликована: Окт. 18, 2024

Язык: Английский

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

Information Sciences, Год журнала: 2024, Номер 681, С. 121215 - 121215

Опубликована: Июль 23, 2024

Язык: Английский

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

Expert Systems with Applications, Год журнала: 2025, Номер 271, С. 126662 - 126662

Опубликована: Янв. 30, 2025

Язык: Английский

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

Information Sciences, Год журнала: 2025, Номер unknown, С. 122047 - 122047

Опубликована: Март 1, 2025

Язык: Английский

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

Computers & Electrical Engineering, Год журнала: 2025, Номер 123, С. 110232 - 110232

Опубликована: Март 15, 2025

Язык: Английский

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

Engineering Construction & Architectural Management, Год журнала: 2025, Номер unknown

Опубликована: Март 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.

Язык: Английский

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Multi-label feature selection via label relaxation

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 113047 - 113047

Опубликована: Март 1, 2025

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

Structural Control and Health Monitoring, Год журнала: 2025, Номер 2025(1)

Опубликована: Янв. 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.

Язык: Английский

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

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 113301 - 113301

Опубликована: Июнь 1, 2025

Язык: Английский

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