Wavelet analysis text classification algorithm based on typical features of data samples

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 achieve optimal performance due to the limitations of their feature extraction and analysis techniques. To address these issues, this paper proposes two novel algorithms: (1) Average Term Frequency-Document Frequency (ATF-DF), which adopts a forward-thinking approach to comprehensively extract category-specific features from labeled text samples, resulting in class feature vectors that effectively represent the text categories; (2) Average Term Frequency-Document Frequency-Wavelet Analysis (ATF-DF-WA), which transforms class feature vectors into waveforms and utilizes wavelet analysis to extract typical class feature layer waveforms and feature layer waveforms of the text to be classified. Text classification is then performed by calculating waveform similarity. Experimental results on the THUCHNews dataset demonstrate that compared to two baseline algorithms, ATF-DF improves Precision, Recall, and F1-score by 13.71%, 28.94%, and 20.74%, respectively. Furthermore, experimental results on the THUCHNews, Sogou, and CNTC datasets indicate that ATF-DF-WA outperforms four baseline algorithms, achieving an average Precision improvement of 2.80% to 80.36%, an average Recall improvement of 0.10% to 54.65%, and an average F1-score improvement of 2.62% to 60.82%. Additionally, experimental results on the THUCHNews dataset reveal that ATF-DF-WA demonstrates advantages in both classification performance and training speed compared to baseline algorithms based on pre-trained models, highlighting its promising potential for practical applications.