High-Quality Train Data Generation for Deep Learning-Based Web Page Classification Models
The current deep learning models detecting relevant web pages show low accuracy because of the poor quality of the training data. In this paper, we propose a novel algorithm to automatically generate high-quality training data based on the frequency of the document including the entity of interest....
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| Veröffentlicht in: | IEEE access 2021, Vol.9, p.85240-85254 |
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| creator | Kim, Jeong-Jae On, Byung-Won Lee, Ingyu |
| description | The current deep learning models detecting relevant web pages show low accuracy because of the poor quality of the training data. In this paper, we propose a novel algorithm to automatically generate high-quality training data based on the frequency of the document including the entity of interest. Our experimental results with movies and cellphones data sets show that the average F_{1} -score of the deep learning models (FNN, CNN, Bi-LSTM, and SeqGAN) trained with our proposed algorithm shows up to 0.9992 in F_{1} -score. |
| doi_str_mv | 10.1109/ACCESS.2021.3086586 |
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In this paper, we propose a novel algorithm to automatically generate high-quality training data based on the frequency of the document including the entity of interest. Our experimental results with movies and cellphones data sets show that the average <inline-formula> <tex-math notation="LaTeX">F_{1} </tex-math></inline-formula>-score of the deep learning models (FNN, CNN, Bi-LSTM, and SeqGAN) trained with our proposed algorithm shows up to 0.9992 in <inline-formula> <tex-math notation="LaTeX">F_{1} </tex-math></inline-formula>-score.]]></description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2021.3086586</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algorithms ; automatic labelling ; Complexity theory ; Data mining ; Data models ; Deep learning ; Machine learning ; Text classification ; Training ; Training data ; Web pages ; Websites</subject><ispartof>IEEE access, 2021, Vol.9, p.85240-85254</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-b69e64ff8502e7ee7090b07206f07f4bd996f0a1f9d4d8c0b4e55b52f26d9b173</citedby><cites>FETCH-LOGICAL-c408t-b69e64ff8502e7ee7090b07206f07f4bd996f0a1f9d4d8c0b4e55b52f26d9b173</cites><orcidid>0000-0002-9629-6816 ; 0000-0001-6929-3188</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9446993$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2100,4022,27632,27922,27923,27924,54932</link.rule.ids></links><search><creatorcontrib>Kim, Jeong-Jae</creatorcontrib><creatorcontrib>On, Byung-Won</creatorcontrib><creatorcontrib>Lee, Ingyu</creatorcontrib><title>High-Quality Train Data Generation for Deep Learning-Based Web Page Classification Models</title><title>IEEE access</title><addtitle>Access</addtitle><description><![CDATA[The current deep learning models detecting relevant web pages show low accuracy because of the poor quality of the training data. In this paper, we propose a novel algorithm to automatically generate high-quality training data based on the frequency of the document including the entity of interest. Our experimental results with movies and cellphones data sets show that the average <inline-formula> <tex-math notation="LaTeX">F_{1} </tex-math></inline-formula>-score of the deep learning models (FNN, CNN, Bi-LSTM, and SeqGAN) trained with our proposed algorithm shows up to 0.9992 in <inline-formula> <tex-math notation="LaTeX">F_{1} </tex-math></inline-formula>-score.]]></description><subject>Algorithms</subject><subject>automatic labelling</subject><subject>Complexity theory</subject><subject>Data mining</subject><subject>Data models</subject><subject>Deep learning</subject><subject>Machine learning</subject><subject>Text classification</subject><subject>Training</subject><subject>Training data</subject><subject>Web pages</subject><subject>Websites</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNUU1r3DAUNCWFhnR_QS6Cnr3Rt6xj6qRJYEtaNqX0JJ6tp60W19pK3kP-fZw4hLzLG4aZeQ-mqs4ZXTNG7cVl215vt2tOOVsL2mjV6A_VKWfa1kIJffIOf6pWpezpPM1MKXNa_bmNu7_1zyMMcXokDxniSK5gAnKDI2aYYhpJSJlcIR7IBiGPcdzVX6GgJ7-xIz9gh6QdoJQYYr_ovyePQ_lcfQwwFFy97rPq17frh_a23tzf3LWXm7qXtJnqTlvUMoRGUY4G0VBLO2o41YGaIDtv7YyABeulb3raSVSqUzxw7W3HjDir7pZcn2DvDjn-g_zoEkT3QqS8c5Cn2A_oQgfU9wG86Y0UGq1plLA9CglGQEPnrC9L1iGn_0csk9unYx7n9x1XkivKjJKzSiyqPqdSMoa3q4y650rcUol7rsS9VjK7zhdXRMQ3h5VSWyvEE6olhpM</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Kim, Jeong-Jae</creator><creator>On, Byung-Won</creator><creator>Lee, Ingyu</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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In this paper, we propose a novel algorithm to automatically generate high-quality training data based on the frequency of the document including the entity of interest. Our experimental results with movies and cellphones data sets show that the average <inline-formula> <tex-math notation="LaTeX">F_{1} </tex-math></inline-formula>-score of the deep learning models (FNN, CNN, Bi-LSTM, and SeqGAN) trained with our proposed algorithm shows up to 0.9992 in <inline-formula> <tex-math notation="LaTeX">F_{1} </tex-math></inline-formula>-score.]]></abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2021.3086586</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-9629-6816</orcidid><orcidid>https://orcid.org/0000-0001-6929-3188</orcidid><oa>free_for_read</oa></addata></record> |
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| source | IEEE Open Access Journals; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals |
| subjects | Algorithms automatic labelling Complexity theory Data mining Data models Deep learning Machine learning Text classification Training Training data Web pages Websites |
| title | High-Quality Train Data Generation for Deep Learning-Based Web Page Classification Models |
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