Comparison of Average Precision Values Across Three Different Datasets.
<p>Comparison of Average Precision Values Across Three Different Datasets.</p>
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2025
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| _version_ | 1852019755867701248 |
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| author | Ming Gao (115719) |
| author2 | Mengshi Li (21464625) Zhi Ling (18928281) Jinhao Zhong (21464628) Han Ding (831540) Qinghua Wu (79148) |
| author2_role | author author author author author |
| author_facet | Ming Gao (115719) Mengshi Li (21464625) Zhi Ling (18928281) Jinhao Zhong (21464628) Han Ding (831540) Qinghua Wu (79148) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ming Gao (115719) Mengshi Li (21464625) Zhi Ling (18928281) Jinhao Zhong (21464628) Han Ding (831540) Qinghua Wu (79148) |
| dc.date.none.fl_str_mv | 2025-06-02T18:08:18Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0319747.t015 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Comparison_of_Average_Precision_Values_Across_Three_Different_Datasets_/29216871 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | 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 %. |
| dc.title.none.fl_str_mv | Comparison of Average Precision Values Across Three Different Datasets. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Comparison of Average Precision Values Across Three Different Datasets.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_82dbdaf058add204fc59cd379512abd2 |
| identifier_str_mv | 10.1371/journal.pone.0319747.t015 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29216871 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Comparison of Average Precision Values Across Three Different Datasets.Ming Gao (115719)Mengshi Li (21464625)Zhi Ling (18928281)Jinhao Zhong (21464628)Han Ding (831540)Qinghua Wu (79148)GeneticsScience PolicySpace ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedxlink "> currentlytwo baseline algorithmsfully capture categoryexisting category labelscomprehensively extract categorycntc datasets indicatecalculating waveform similaritybaseline algorithms basedclass feature vectorswa demonstrates advantagesaverage precision improvement74 %, respectively71 %, 28thuchnews dataset revealthuchnews dataset demonstrateaverage term frequencylabeled text sampleshandling text datatraining speed compareddf improves precisionutilizes wavelet analysisfeature layer waveformsaverage recall improvementwavelet analysisfeature extractionwa ),document frequency94 %,65 %,36 %,analysis techniquesaverage f1text classificationtext categoriesscore improvementtypical featurestrained modelsthinking approachspecific featurespromising potentialexperimental resultseffectively representdf ),classification performance82 %.<p>Comparison of Average Precision Values Across Three Different Datasets.</p>2025-06-02T18:08:18ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0319747.t015https://figshare.com/articles/dataset/Comparison_of_Average_Precision_Values_Across_Three_Different_Datasets_/29216871CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/292168712025-06-02T18:08:18Z |
| spellingShingle | Comparison of Average Precision Values Across Three Different Datasets. Ming Gao (115719) 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 %. |
| status_str | publishedVersion |
| title | Comparison of Average Precision Values Across Three Different Datasets. |
| title_full | Comparison of Average Precision Values Across Three Different Datasets. |
| title_fullStr | Comparison of Average Precision Values Across Three Different Datasets. |
| title_full_unstemmed | Comparison of Average Precision Values Across Three Different Datasets. |
| title_short | Comparison of Average Precision Values Across Three Different Datasets. |
| title_sort | Comparison of Average Precision Values Across Three Different Datasets. |
| topic | 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 %. |