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Local neighborhood rough set
•Local neighborhood rough set (LNRS) is proposed.•The LNRS model can handle big data with numeric attributes and limited labels.•The corresponding concept approximation and attribute reduction algorithms have linear time complexity.•The study provides a bridge between neighborhood rough set and loca...
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Published in: | Knowledge-based systems 2018-08, Vol.153, p.53-64 |
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container_title | Knowledge-based systems |
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creator | Wang, Qi Qian, Yuhua Liang, Xinyan Guo, Qian Liang, Jiye |
description | •Local neighborhood rough set (LNRS) is proposed.•The LNRS model can handle big data with numeric attributes and limited labels.•The corresponding concept approximation and attribute reduction algorithms have linear time complexity.•The study provides a bridge between neighborhood rough set and local rough set.
With the advent of the age of big data, a typical big data set called limited labeled big data appears. It includes a small amount of labeled data and a large amount of unlabeled data. Some existing neighborhood-based rough set algorithms work well in analyzing the rough data with numerical features. But, they face three challenges: limited labeled property of big data, computational inefficiency and over-fitting in attribute reduction when dealing with limited labeled data. In order to address the three issues, a combination of neighborhood rough set and local rough set called local neighborhood rough set (LNRS) is proposed in this paper. The corresponding concept approximation and attribute reduction algorithms designed with linear time complexity can efficiently and effectively deal with limited labeled big data. The experimental results show that the proposed local neighborhood rough set and corresponding algorithms significantly outperform its original counterpart in classical neighborhood rough set. These results will enrich the local rough set theory and enlarge its application scopes. |
doi_str_mv | 10.1016/j.knosys.2018.04.023 |
format | article |
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With the advent of the age of big data, a typical big data set called limited labeled big data appears. It includes a small amount of labeled data and a large amount of unlabeled data. Some existing neighborhood-based rough set algorithms work well in analyzing the rough data with numerical features. But, they face three challenges: limited labeled property of big data, computational inefficiency and over-fitting in attribute reduction when dealing with limited labeled data. In order to address the three issues, a combination of neighborhood rough set and local rough set called local neighborhood rough set (LNRS) is proposed in this paper. The corresponding concept approximation and attribute reduction algorithms designed with linear time complexity can efficiently and effectively deal with limited labeled big data. The experimental results show that the proposed local neighborhood rough set and corresponding algorithms significantly outperform its original counterpart in classical neighborhood rough set. These results will enrich the local rough set theory and enlarge its application scopes.</description><identifier>ISSN: 0950-7051</identifier><identifier>EISSN: 1872-7409</identifier><identifier>DOI: 10.1016/j.knosys.2018.04.023</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Algorithms ; Attribute reduction ; Big Data ; Cloud computing ; Cognition & reasoning ; Concept approximation ; Data management ; Data mining ; Limited labeled data ; Local neighborhood rough set ; Reduction ; Rough set ; Rough set models ; Set theory</subject><ispartof>Knowledge-based systems, 2018-08, Vol.153, p.53-64</ispartof><rights>2018 Elsevier B.V.</rights><rights>Copyright Elsevier Science Ltd. Aug 1, 2018</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c334t-a082790c2b93535f5eb866d70ba0fc72c346bc8f98cff73dfe210e50ce857cf83</citedby><cites>FETCH-LOGICAL-c334t-a082790c2b93535f5eb866d70ba0fc72c346bc8f98cff73dfe210e50ce857cf83</cites><orcidid>0000-0001-5887-9327 ; 0000-0001-6772-4247</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925,34135</link.rule.ids></links><search><creatorcontrib>Wang, Qi</creatorcontrib><creatorcontrib>Qian, Yuhua</creatorcontrib><creatorcontrib>Liang, Xinyan</creatorcontrib><creatorcontrib>Guo, Qian</creatorcontrib><creatorcontrib>Liang, Jiye</creatorcontrib><title>Local neighborhood rough set</title><title>Knowledge-based systems</title><description>•Local neighborhood rough set (LNRS) is proposed.•The LNRS model can handle big data with numeric attributes and limited labels.•The corresponding concept approximation and attribute reduction algorithms have linear time complexity.•The study provides a bridge between neighborhood rough set and local rough set.
With the advent of the age of big data, a typical big data set called limited labeled big data appears. It includes a small amount of labeled data and a large amount of unlabeled data. Some existing neighborhood-based rough set algorithms work well in analyzing the rough data with numerical features. But, they face three challenges: limited labeled property of big data, computational inefficiency and over-fitting in attribute reduction when dealing with limited labeled data. In order to address the three issues, a combination of neighborhood rough set and local rough set called local neighborhood rough set (LNRS) is proposed in this paper. The corresponding concept approximation and attribute reduction algorithms designed with linear time complexity can efficiently and effectively deal with limited labeled big data. The experimental results show that the proposed local neighborhood rough set and corresponding algorithms significantly outperform its original counterpart in classical neighborhood rough set. These results will enrich the local rough set theory and enlarge its application scopes.</description><subject>Algorithms</subject><subject>Attribute reduction</subject><subject>Big Data</subject><subject>Cloud computing</subject><subject>Cognition & reasoning</subject><subject>Concept approximation</subject><subject>Data management</subject><subject>Data mining</subject><subject>Limited labeled data</subject><subject>Local neighborhood rough set</subject><subject>Reduction</subject><subject>Rough set</subject><subject>Rough set models</subject><subject>Set theory</subject><issn>0950-7051</issn><issn>1872-7409</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>F2A</sourceid><recordid>eNp9kM1OwzAQhC0EEqXwBj1E4pywtuPYuSChij-pEhc4W8lm3SSUuNgpUt-eVOHMZfYyM6v5GFtxyDjw4q7PPgcfjzETwE0GeQZCnrEFN1qkOofynC2gVJBqUPySXcXYA4AQ3CzYauOx2iUDddu29qH1vkmCP2zbJNJ4zS5ctYt083eX7OPp8X39km7enl_XD5sUpczHtAIjdAko6lIqqZyi2hRFo6GuwKEWKPOiRuNKg85p2TgSHEgBklEanZFLdjv37oP_PlAcbe8PYZheWgHGgOGTTK58dmHwMQZydh-6ryocLQd74mB7O3OwJw4WcjtxmGL3c4ymBT8dBRuxowGp6QLhaBvf_V_wC7CXZw8</recordid><startdate>20180801</startdate><enddate>20180801</enddate><creator>Wang, Qi</creator><creator>Qian, Yuhua</creator><creator>Liang, Xinyan</creator><creator>Guo, Qian</creator><creator>Liang, Jiye</creator><general>Elsevier B.V</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>E3H</scope><scope>F2A</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0001-5887-9327</orcidid><orcidid>https://orcid.org/0000-0001-6772-4247</orcidid></search><sort><creationdate>20180801</creationdate><title>Local neighborhood rough set</title><author>Wang, Qi ; Qian, Yuhua ; Liang, Xinyan ; Guo, Qian ; Liang, Jiye</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c334t-a082790c2b93535f5eb866d70ba0fc72c346bc8f98cff73dfe210e50ce857cf83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Algorithms</topic><topic>Attribute reduction</topic><topic>Big Data</topic><topic>Cloud computing</topic><topic>Cognition & reasoning</topic><topic>Concept approximation</topic><topic>Data management</topic><topic>Data mining</topic><topic>Limited labeled data</topic><topic>Local neighborhood rough set</topic><topic>Reduction</topic><topic>Rough set</topic><topic>Rough set models</topic><topic>Set theory</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Qi</creatorcontrib><creatorcontrib>Qian, Yuhua</creatorcontrib><creatorcontrib>Liang, Xinyan</creatorcontrib><creatorcontrib>Guo, Qian</creatorcontrib><creatorcontrib>Liang, Jiye</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>Library & Information Sciences Abstracts (LISA)</collection><collection>Library & Information Science Abstracts (LISA)</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Knowledge-based systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Qi</au><au>Qian, Yuhua</au><au>Liang, Xinyan</au><au>Guo, Qian</au><au>Liang, Jiye</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Local neighborhood rough set</atitle><jtitle>Knowledge-based systems</jtitle><date>2018-08-01</date><risdate>2018</risdate><volume>153</volume><spage>53</spage><epage>64</epage><pages>53-64</pages><issn>0950-7051</issn><eissn>1872-7409</eissn><abstract>•Local neighborhood rough set (LNRS) is proposed.•The LNRS model can handle big data with numeric attributes and limited labels.•The corresponding concept approximation and attribute reduction algorithms have linear time complexity.•The study provides a bridge between neighborhood rough set and local rough set.
With the advent of the age of big data, a typical big data set called limited labeled big data appears. It includes a small amount of labeled data and a large amount of unlabeled data. Some existing neighborhood-based rough set algorithms work well in analyzing the rough data with numerical features. But, they face three challenges: limited labeled property of big data, computational inefficiency and over-fitting in attribute reduction when dealing with limited labeled data. In order to address the three issues, a combination of neighborhood rough set and local rough set called local neighborhood rough set (LNRS) is proposed in this paper. The corresponding concept approximation and attribute reduction algorithms designed with linear time complexity can efficiently and effectively deal with limited labeled big data. The experimental results show that the proposed local neighborhood rough set and corresponding algorithms significantly outperform its original counterpart in classical neighborhood rough set. These results will enrich the local rough set theory and enlarge its application scopes.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.knosys.2018.04.023</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0001-5887-9327</orcidid><orcidid>https://orcid.org/0000-0001-6772-4247</orcidid></addata></record> |
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language | eng |
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source | Library & Information Science Abstracts (LISA); ScienceDirect Journals |
subjects | Algorithms Attribute reduction Big Data Cloud computing Cognition & reasoning Concept approximation Data management Data mining Limited labeled data Local neighborhood rough set Reduction Rough set Rough set models Set theory |
title | Local neighborhood rough set |
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