FeaLearner: A Novel Framework of Self-Adaptive Feature Learning and Selection for Suicide Risk Detection from Users’ Social Media Posts

Zhang, X; Sheng, Y; He, L; Liu, Ming; Lai, X

FeaLearner: A Novel Framework of Self-Adaptive Feature Learning and Selection for Suicide Risk Detection from Users’ Social Media Posts

https://hdl.handle.net/10779/DRO/DU:30651845

conference contribution

posted on 2025-11-19, 00:51 authored by X Zhang, Y Sheng, L He, Ming LiuMing Liu, X Lai

FeaLearner: A Novel Framework of Self-Adaptive Feature Learning and Selection for Suicide Risk Detection from Users’ Social Media Posts

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DOI - Is version of https://doi.org/10.1007/978-981-95-3456-2_15

Location

Kyoto, Japan

Open access

No

Language

eng

Volume

16198

Pagination

207-222

Start date

2025-10-22

End date

2025-10-24

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9789819534555

Title of proceedings

ADMA 2025 : Proceedings of the 21st International Conference on Advanced Data Mining and Applications

Event

Advanced Data Mining and Applications. Conference (2025 : 21st : Kyoto, Japan)

Publisher

Springer Nature

Series

Lecture Notes in Computer Science

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Keywords

Behavioral and Social Science Mental Health Prevention Suicide Suicide Prevention Generic health relevance

FeaLearner: A Novel Framework of Self-Adaptive Feature Learning and Selection for Suicide Risk Detection from Users’ Social Media Posts

History

Related Materials

Location

Open access

Language

Volume

Pagination

Start date

End date

ISSN

eISSN

ISBN-13

Title of proceedings

Event

Publisher

Series

Usage metrics

Categories

Keywords

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