ATF-DF Algorithm Framework Diagram.

<div><p>Currently, traditional text feature extraction methods fail to fully capture category-specific features when handling text data with existing category labels, thereby limiting classification performance. Meanwhile, text classification methods based on wavelet analysis have yet to...

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Bibliographic Details
Main Author:	Ming Gao (115719) (author)
Other Authors:	Mengshi Li (21464625) (author), Zhi Ling (18928281) (author), Jinhao Zhong (21464628) (author), Han Ding (831540) (author), Qinghua Wu (79148) (author)
Published:	2025
Subjects:	Genetics Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> currently two baseline algorithms fully capture category existing category labels comprehensively extract category cntc datasets indicate calculating waveform similarity baseline algorithms based class feature vectors wa demonstrates advantages average precision improvement 74 %, respectively 71 %, 28 thuchnews dataset reveal thuchnews dataset demonstrate average term frequency labeled text samples handling text data training speed compared df improves precision utilizes wavelet analysis feature layer waveforms average recall improvement wavelet analysis feature extraction wa ), document frequency 94 %, 65 %, 36 %, analysis techniques average f1 text classification text categories score improvement typical features trained models thinking approach specific features promising potential experimental results effectively represent df ), classification performance 82 %.
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ATF-DF Algorithm Framework Diagram.

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