Company identification method and device based on multi-dimensional holographic features and logistic regression
The invention discloses a company identification method and device based on multi-dimensional holographic features and logistic regression. Comprising the following steps: S1, carrying out risk labeling and preprocessing on an enterprise data set; s2, extracting features required by modeling from th...
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Patent |
| Sprache: | chi ; eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | ZHAO ZHIHANG RONG GUANGSHENG PAN XINBING WEI JINLEI WANG GONGMING |
| description | The invention discloses a company identification method and device based on multi-dimensional holographic features and logistic regression. Comprising the following steps: S1, carrying out risk labeling and preprocessing on an enterprise data set; s2, extracting features required by modeling from the enterprise data set; s3, training a logistic regression model LR by using the extracted features and the labeling result; and S4, judging whether the enterprise is an empty shell company or not by using the trained logistic regression model LR. The method has the beneficial effects that the multi-dimensional holographic features covering all aspects of the enterprise are collected, the full view of the enterprise can be fully reflected, the biases and unfairness of modeling by adopting a single feature in a traditional method are overcome, and the application range is wide; a logistic regression method with self-learning ability, robustness and stability is adopted, limitation of a rule model and subjectivity of |
| format | Patent |
| fullrecord | <record><control><sourceid>epo_EVB</sourceid><recordid>TN_cdi_epo_espacenet_CN116186172A</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>CN116186172A</sourcerecordid><originalsourceid>FETCH-epo_espacenet_CN116186172A3</originalsourceid><addsrcrecordid>eNqNi7EKwjAURbs4iPoPzw_oEIXqWori5ORenslt-yBNQpMK_r2p-AFOF849Z12Exo-B3ZvEwCXpRHMS72hEGrwhdoYMXqJBT44wtFyzTVIaGeFiVtnS4K3vJw6DaOrAaZ4Qv2nGElOmE_rMFn1brDq2Ebvfbor99fJobiWCbxEDaziktrkrValzpU6H-viP8wG66UQm</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>patent</recordtype></control><display><type>patent</type><title>Company identification method and device based on multi-dimensional holographic features and logistic regression</title><source>esp@cenet</source><creator>ZHAO ZHIHANG ; RONG GUANGSHENG ; PAN XINBING ; WEI JINLEI ; WANG GONGMING</creator><creatorcontrib>ZHAO ZHIHANG ; RONG GUANGSHENG ; PAN XINBING ; WEI JINLEI ; WANG GONGMING</creatorcontrib><description>The invention discloses a company identification method and device based on multi-dimensional holographic features and logistic regression. Comprising the following steps: S1, carrying out risk labeling and preprocessing on an enterprise data set; s2, extracting features required by modeling from the enterprise data set; s3, training a logistic regression model LR by using the extracted features and the labeling result; and S4, judging whether the enterprise is an empty shell company or not by using the trained logistic regression model LR. The method has the beneficial effects that the multi-dimensional holographic features covering all aspects of the enterprise are collected, the full view of the enterprise can be fully reflected, the biases and unfairness of modeling by adopting a single feature in a traditional method are overcome, and the application range is wide; a logistic regression method with self-learning ability, robustness and stability is adopted, limitation of a rule model and subjectivity of</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTING ; COUNTING ; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ; ELECTRIC DIGITAL DATA PROCESSING ; PHYSICS ; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><creationdate>2023</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230530&DB=EPODOC&CC=CN&NR=116186172A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20230530&DB=EPODOC&CC=CN&NR=116186172A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>ZHAO ZHIHANG</creatorcontrib><creatorcontrib>RONG GUANGSHENG</creatorcontrib><creatorcontrib>PAN XINBING</creatorcontrib><creatorcontrib>WEI JINLEI</creatorcontrib><creatorcontrib>WANG GONGMING</creatorcontrib><title>Company identification method and device based on multi-dimensional holographic features and logistic regression</title><description>The invention discloses a company identification method and device based on multi-dimensional holographic features and logistic regression. Comprising the following steps: S1, carrying out risk labeling and preprocessing on an enterprise data set; s2, extracting features required by modeling from the enterprise data set; s3, training a logistic regression model LR by using the extracted features and the labeling result; and S4, judging whether the enterprise is an empty shell company or not by using the trained logistic regression model LR. The method has the beneficial effects that the multi-dimensional holographic features covering all aspects of the enterprise are collected, the full view of the enterprise can be fully reflected, the biases and unfairness of modeling by adopting a single feature in a traditional method are overcome, and the application range is wide; a logistic regression method with self-learning ability, robustness and stability is adopted, limitation of a rule model and subjectivity of</description><subject>CALCULATING</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</subject><subject>ELECTRIC DIGITAL DATA PROCESSING</subject><subject>PHYSICS</subject><subject>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2023</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNqNi7EKwjAURbs4iPoPzw_oEIXqWori5ORenslt-yBNQpMK_r2p-AFOF849Z12Exo-B3ZvEwCXpRHMS72hEGrwhdoYMXqJBT44wtFyzTVIaGeFiVtnS4K3vJw6DaOrAaZ4Qv2nGElOmE_rMFn1brDq2Ebvfbor99fJobiWCbxEDaziktrkrValzpU6H-viP8wG66UQm</recordid><startdate>20230530</startdate><enddate>20230530</enddate><creator>ZHAO ZHIHANG</creator><creator>RONG GUANGSHENG</creator><creator>PAN XINBING</creator><creator>WEI JINLEI</creator><creator>WANG GONGMING</creator><scope>EVB</scope></search><sort><creationdate>20230530</creationdate><title>Company identification method and device based on multi-dimensional holographic features and logistic regression</title><author>ZHAO ZHIHANG ; RONG GUANGSHENG ; PAN XINBING ; WEI JINLEI ; WANG GONGMING</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN116186172A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2023</creationdate><topic>CALCULATING</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES</topic><topic>ELECTRIC DIGITAL DATA PROCESSING</topic><topic>PHYSICS</topic><topic>SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR</topic><toplevel>online_resources</toplevel><creatorcontrib>ZHAO ZHIHANG</creatorcontrib><creatorcontrib>RONG GUANGSHENG</creatorcontrib><creatorcontrib>PAN XINBING</creatorcontrib><creatorcontrib>WEI JINLEI</creatorcontrib><creatorcontrib>WANG GONGMING</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>ZHAO ZHIHANG</au><au>RONG GUANGSHENG</au><au>PAN XINBING</au><au>WEI JINLEI</au><au>WANG GONGMING</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Company identification method and device based on multi-dimensional holographic features and logistic regression</title><date>2023-05-30</date><risdate>2023</risdate><abstract>The invention discloses a company identification method and device based on multi-dimensional holographic features and logistic regression. Comprising the following steps: S1, carrying out risk labeling and preprocessing on an enterprise data set; s2, extracting features required by modeling from the enterprise data set; s3, training a logistic regression model LR by using the extracted features and the labeling result; and S4, judging whether the enterprise is an empty shell company or not by using the trained logistic regression model LR. The method has the beneficial effects that the multi-dimensional holographic features covering all aspects of the enterprise are collected, the full view of the enterprise can be fully reflected, the biases and unfairness of modeling by adopting a single feature in a traditional method are overcome, and the application range is wide; a logistic regression method with self-learning ability, robustness and stability is adopted, limitation of a rule model and subjectivity of</abstract><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | |
| ispartof | |
| issn | |
| language | chi ; eng |
| recordid | cdi_epo_espacenet_CN116186172A |
| source | esp@cenet |
| subjects | CALCULATING COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES ELECTRIC DIGITAL DATA PROCESSING PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | Company identification method and device based on multi-dimensional holographic features and logistic regression |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-06T23%3A01%3A23IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-epo_EVB&rft_val_fmt=info:ofi/fmt:kev:mtx:patent&rft.genre=patent&rft.au=ZHAO%20ZHIHANG&rft.date=2023-05-30&rft_id=info:doi/&rft_dat=%3Cepo_EVB%3ECN116186172A%3C/epo_EVB%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |