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Hierarchical online streaming feature selection based on adaptive ReliefF
In hierarchical classification learning, the feature space of data has high dimensionality, and the feature space is unknown with emerging features. To solve the above problems, we proposes an online streaming feature selection algorithm for hierarchical classification based on the adaptive ReliefF....
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| creator | Wang, Chenxi Ren, Mengli E, Chen Yu, Xiehua Lin, Yaojin Li, Shaozi |
| description | In hierarchical classification learning, the feature space of data has high dimensionality, and the feature space is unknown with emerging features. To solve the above problems, we proposes an online streaming feature selection algorithm for hierarchical classification based on the adaptive ReliefF. Firstly, the ReliefF is adaptively improved by using the density information of instances around the sample, making it unnecessary to pre-specify parameters. Secondly, the hierarchical relationship between classes is integrated into the algorithm, and a new method for calculating the feature weight of hierarchical data is defined. Then, a feature online importance analysis method is designed based on feature weight. Finally, the adaptive ReliefF algorithm is improved based on feature redundancy, and the feature weight is scaled by the correlation between the features to achieve dynamic feature redundancy update. A large number of experiments verify the effectiveness of the proposed algorithm. |
| doi_str_mv | 10.1109/ITME56794.2022.00131 |
| format | conference_proceeding |
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To solve the above problems, we proposes an online streaming feature selection algorithm for hierarchical classification based on the adaptive ReliefF. Firstly, the ReliefF is adaptively improved by using the density information of instances around the sample, making it unnecessary to pre-specify parameters. Secondly, the hierarchical relationship between classes is integrated into the algorithm, and a new method for calculating the feature weight of hierarchical data is defined. Then, a feature online importance analysis method is designed based on feature weight. Finally, the adaptive ReliefF algorithm is improved based on feature redundancy, and the feature weight is scaled by the correlation between the features to achieve dynamic feature redundancy update. A large number of experiments verify the effectiveness of the proposed algorithm.</description><identifier>EISSN: 2474-3828</identifier><identifier>EISBN: 9798350310153</identifier><identifier>DOI: 10.1109/ITME56794.2022.00131</identifier><identifier>CODEN: IEEPAD</identifier><language>eng</language><publisher>IEEE</publisher><subject>adaptive ReliefF algorithm ; Classification algorithms ; Correlation ; Education ; Feature extraction ; feature selection ; Heuristic algorithms ; hierarchical classification ; Information technology ; online feature selection ; Redundancy ; weight scaling</subject><ispartof>2022 12th International Conference on Information Technology in Medicine and Education (ITME)v, 2022, p.613-617</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10086273$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,27916,54546,54923</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10086273$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Wang, Chenxi</creatorcontrib><creatorcontrib>Ren, Mengli</creatorcontrib><creatorcontrib>E, Chen</creatorcontrib><creatorcontrib>Yu, Xiehua</creatorcontrib><creatorcontrib>Lin, Yaojin</creatorcontrib><creatorcontrib>Li, Shaozi</creatorcontrib><title>Hierarchical online streaming feature selection based on adaptive ReliefF</title><title>2022 12th International Conference on Information Technology in Medicine and Education (ITME)v</title><addtitle>ITME</addtitle><description>In hierarchical classification learning, the feature space of data has high dimensionality, and the feature space is unknown with emerging features. 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A large number of experiments verify the effectiveness of the proposed algorithm.</description><subject>adaptive ReliefF algorithm</subject><subject>Classification algorithms</subject><subject>Correlation</subject><subject>Education</subject><subject>Feature extraction</subject><subject>feature selection</subject><subject>Heuristic algorithms</subject><subject>hierarchical classification</subject><subject>Information technology</subject><subject>online feature selection</subject><subject>Redundancy</subject><subject>weight scaling</subject><issn>2474-3828</issn><isbn>9798350310153</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2022</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotjtFKw0AQRVdBsNT8QR_yA4kzO5vd5FFKawMtgtTnMtnM6kqaliQK_r0BfbkHLofLVWqFkCNC9VgfD5vCusrkGrTOAZDwRiWVq0oqgBCwoFu10MaZjEpd3qtkHD9h9qwGq-1C1bsoAw_-I3ru0kvfxV7ScRqEz7F_T4Pw9DXMjXTip3jp04ZHaWcx5ZavU_yW9FW6KGH7oO4Cd6Mk_1yqt-3muN5l-5fnev20zyJiNWUIbQhEiNpY49jPxwvy0BgpxTMEQ-ga13hNRZgDOei2seTQ2SCNYVqq1d9uFJHTdYhnHn5OCFBa7Yh-AbevTmI</recordid><startdate>202211</startdate><enddate>202211</enddate><creator>Wang, Chenxi</creator><creator>Ren, Mengli</creator><creator>E, Chen</creator><creator>Yu, Xiehua</creator><creator>Lin, Yaojin</creator><creator>Li, Shaozi</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>202211</creationdate><title>Hierarchical online streaming feature selection based on adaptive ReliefF</title><author>Wang, Chenxi ; Ren, Mengli ; E, Chen ; Yu, Xiehua ; Lin, Yaojin ; Li, Shaozi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i119t-10dff331124647ac20253c0b4e8eca0f4317b7bc235fc231af2db637176feb4a3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2022</creationdate><topic>adaptive ReliefF algorithm</topic><topic>Classification algorithms</topic><topic>Correlation</topic><topic>Education</topic><topic>Feature extraction</topic><topic>feature selection</topic><topic>Heuristic algorithms</topic><topic>hierarchical classification</topic><topic>Information technology</topic><topic>online feature selection</topic><topic>Redundancy</topic><topic>weight scaling</topic><toplevel>online_resources</toplevel><creatorcontrib>Wang, Chenxi</creatorcontrib><creatorcontrib>Ren, Mengli</creatorcontrib><creatorcontrib>E, Chen</creatorcontrib><creatorcontrib>Yu, Xiehua</creatorcontrib><creatorcontrib>Lin, Yaojin</creatorcontrib><creatorcontrib>Li, Shaozi</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wang, Chenxi</au><au>Ren, Mengli</au><au>E, Chen</au><au>Yu, Xiehua</au><au>Lin, Yaojin</au><au>Li, Shaozi</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Hierarchical online streaming feature selection based on adaptive ReliefF</atitle><btitle>2022 12th International Conference on Information Technology in Medicine and Education (ITME)v</btitle><stitle>ITME</stitle><date>2022-11</date><risdate>2022</risdate><spage>613</spage><epage>617</epage><pages>613-617</pages><eissn>2474-3828</eissn><eisbn>9798350310153</eisbn><coden>IEEPAD</coden><abstract>In hierarchical classification learning, the feature space of data has high dimensionality, and the feature space is unknown with emerging features. To solve the above problems, we proposes an online streaming feature selection algorithm for hierarchical classification based on the adaptive ReliefF. Firstly, the ReliefF is adaptively improved by using the density information of instances around the sample, making it unnecessary to pre-specify parameters. Secondly, the hierarchical relationship between classes is integrated into the algorithm, and a new method for calculating the feature weight of hierarchical data is defined. Then, a feature online importance analysis method is designed based on feature weight. Finally, the adaptive ReliefF algorithm is improved based on feature redundancy, and the feature weight is scaled by the correlation between the features to achieve dynamic feature redundancy update. A large number of experiments verify the effectiveness of the proposed algorithm.</abstract><pub>IEEE</pub><doi>10.1109/ITME56794.2022.00131</doi><tpages>5</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | EISSN: 2474-3828 |
| ispartof | 2022 12th International Conference on Information Technology in Medicine and Education (ITME)v, 2022, p.613-617 |
| issn | 2474-3828 |
| language | eng |
| recordid | cdi_ieee_primary_10086273 |
| source | IEEE Xplore All Conference Series |
| subjects | adaptive ReliefF algorithm Classification algorithms Correlation Education Feature extraction feature selection Heuristic algorithms hierarchical classification Information technology online feature selection Redundancy weight scaling |
| title | Hierarchical online streaming feature selection based on adaptive ReliefF |
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