ATISA: Adaptive Threshold-based Instance Selection Algorithm
•We propose three instance reduction techniques called ATISA1,2,3.•ATISA maintain important border and inner points per class based on an adaptive threshold.•When compared with the state-of-the-art algorithms, ATISA1 obtains better accuracy rates and promising reduction rates.•ATISA is faster than D...
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| Veröffentlicht in: | Expert systems with applications 2013-12, Vol.40 (17), p.6894-6900 |
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| container_title | Expert systems with applications |
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| creator | Cavalcanti, George D.C. Ren, Tsang Ing Pereira, Cesar Lima |
| description | •We propose three instance reduction techniques called ATISA1,2,3.•ATISA maintain important border and inner points per class based on an adaptive threshold.•When compared with the state-of-the-art algorithms, ATISA1 obtains better accuracy rates and promising reduction rates.•ATISA is faster than DROP3, ICF and HMN-EI.
Instance reduction techniques can improve generalization, reduce storage requirements and execution time of instance-based learning algorithms. This paper presents an instance reduction algorithm called Adaptive Threshold-based Instance Selection Algorithm (ATISA). ATISA aims to preserve important instances based on a selection criterion that uses the distance of each instance to its nearest enemy as a threshold. This threshold defines the coverage area of each instance that is given by a hyper-sphere centered at it. The experimental results show the effectiveness, in terms of accuracy, reduction rate, and computational time, of the ATISA algorithm when compared with state-of-the-art reduction algorithms. |
| doi_str_mv | 10.1016/j.eswa.2013.06.053 |
| format | Article |
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Instance reduction techniques can improve generalization, reduce storage requirements and execution time of instance-based learning algorithms. This paper presents an instance reduction algorithm called Adaptive Threshold-based Instance Selection Algorithm (ATISA). ATISA aims to preserve important instances based on a selection criterion that uses the distance of each instance to its nearest enemy as a threshold. This threshold defines the coverage area of each instance that is given by a hyper-sphere centered at it. The experimental results show the effectiveness, in terms of accuracy, reduction rate, and computational time, of the ATISA algorithm when compared with state-of-the-art reduction algorithms.</description><identifier>ISSN: 0957-4174</identifier><identifier>EISSN: 1873-6793</identifier><identifier>DOI: 10.1016/j.eswa.2013.06.053</identifier><language>eng</language><publisher>Amsterdam: Elsevier Ltd</publisher><subject>Adaptive algorithms ; Algorithms ; Applied sciences ; Artificial intelligence ; Computer science; control theory; systems ; Data processing. List processing. Character string processing ; Exact sciences and technology ; Expert systems ; Instance selection ; Instance-based learning algorithms ; Learning ; Memory organisation. Data processing ; Preserves ; Reduction ; Software ; State of the art ; Thresholds</subject><ispartof>Expert systems with applications, 2013-12, Vol.40 (17), p.6894-6900</ispartof><rights>2013 Elsevier Ltd</rights><rights>2014 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c396t-efbeb252115be6779834c80d0c0cf4194b95aeda2f082c20279f6bbe4f6c51563</citedby><cites>FETCH-LOGICAL-c396t-efbeb252115be6779834c80d0c0cf4194b95aeda2f082c20279f6bbe4f6c51563</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S095741741300448X$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27667404$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Cavalcanti, George D.C.</creatorcontrib><creatorcontrib>Ren, Tsang Ing</creatorcontrib><creatorcontrib>Pereira, Cesar Lima</creatorcontrib><title>ATISA: Adaptive Threshold-based Instance Selection Algorithm</title><title>Expert systems with applications</title><description>•We propose three instance reduction techniques called ATISA1,2,3.•ATISA maintain important border and inner points per class based on an adaptive threshold.•When compared with the state-of-the-art algorithms, ATISA1 obtains better accuracy rates and promising reduction rates.•ATISA is faster than DROP3, ICF and HMN-EI.
Instance reduction techniques can improve generalization, reduce storage requirements and execution time of instance-based learning algorithms. This paper presents an instance reduction algorithm called Adaptive Threshold-based Instance Selection Algorithm (ATISA). ATISA aims to preserve important instances based on a selection criterion that uses the distance of each instance to its nearest enemy as a threshold. This threshold defines the coverage area of each instance that is given by a hyper-sphere centered at it. The experimental results show the effectiveness, in terms of accuracy, reduction rate, and computational time, of the ATISA algorithm when compared with state-of-the-art reduction algorithms.</description><subject>Adaptive algorithms</subject><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Data processing. List processing. Character string processing</subject><subject>Exact sciences and technology</subject><subject>Expert systems</subject><subject>Instance selection</subject><subject>Instance-based learning algorithms</subject><subject>Learning</subject><subject>Memory organisation. Data processing</subject><subject>Preserves</subject><subject>Reduction</subject><subject>Software</subject><subject>State of the art</subject><subject>Thresholds</subject><issn>0957-4174</issn><issn>1873-6793</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNqF0D1PwzAQgGELgUQp_AGmLEgsCeeP2AliiSo-KlViaJktx7lQV2lT7FDEv8dVK0aYvLx3Jz-EXFPIKFB5t8owfJmMAeUZyAxyfkJGtFA8larkp2QEZa5SQZU4JxchrACoAlAj8lAtpvPqPqkasx3cDpPF0mNY9l2T1iZgk0w3YTAbi8kcO7SD6zdJ1b333g3L9SU5a00X8Or4jsnb0-Ni8pLOXp-nk2qWWl7KIcW2xprljNK8RqlUWXBhC2jAgm0FLUVd5gYbw1oomGXAVNnKukbRSpvTXPIxuT3s3fr-4xPDoNcuWOw6s8H-M-j4FwoKpMj_T4UoFJWcQ0zZIbW-D8Fjq7ferY3_1hT0XlWv9F5V71U1SB1V49DNcb8J1nStjzYu_E4yJaUSIGL3cOgwuuwceh2sw-jYOB8ZddO7v878APt7i6k</recordid><startdate>20131201</startdate><enddate>20131201</enddate><creator>Cavalcanti, George D.C.</creator><creator>Ren, Tsang Ing</creator><creator>Pereira, Cesar Lima</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20131201</creationdate><title>ATISA: Adaptive Threshold-based Instance Selection Algorithm</title><author>Cavalcanti, George D.C. ; Ren, Tsang Ing ; Pereira, Cesar Lima</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c396t-efbeb252115be6779834c80d0c0cf4194b95aeda2f082c20279f6bbe4f6c51563</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Adaptive algorithms</topic><topic>Algorithms</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Data processing. List processing. Character string processing</topic><topic>Exact sciences and technology</topic><topic>Expert systems</topic><topic>Instance selection</topic><topic>Instance-based learning algorithms</topic><topic>Learning</topic><topic>Memory organisation. Data processing</topic><topic>Preserves</topic><topic>Reduction</topic><topic>Software</topic><topic>State of the art</topic><topic>Thresholds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cavalcanti, George D.C.</creatorcontrib><creatorcontrib>Ren, Tsang Ing</creatorcontrib><creatorcontrib>Pereira, Cesar Lima</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</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>Expert systems with applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cavalcanti, George D.C.</au><au>Ren, Tsang Ing</au><au>Pereira, Cesar Lima</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>ATISA: Adaptive Threshold-based Instance Selection Algorithm</atitle><jtitle>Expert systems with applications</jtitle><date>2013-12-01</date><risdate>2013</risdate><volume>40</volume><issue>17</issue><spage>6894</spage><epage>6900</epage><pages>6894-6900</pages><issn>0957-4174</issn><eissn>1873-6793</eissn><abstract>•We propose three instance reduction techniques called ATISA1,2,3.•ATISA maintain important border and inner points per class based on an adaptive threshold.•When compared with the state-of-the-art algorithms, ATISA1 obtains better accuracy rates and promising reduction rates.•ATISA is faster than DROP3, ICF and HMN-EI.
Instance reduction techniques can improve generalization, reduce storage requirements and execution time of instance-based learning algorithms. This paper presents an instance reduction algorithm called Adaptive Threshold-based Instance Selection Algorithm (ATISA). ATISA aims to preserve important instances based on a selection criterion that uses the distance of each instance to its nearest enemy as a threshold. This threshold defines the coverage area of each instance that is given by a hyper-sphere centered at it. The experimental results show the effectiveness, in terms of accuracy, reduction rate, and computational time, of the ATISA algorithm when compared with state-of-the-art reduction algorithms.</abstract><cop>Amsterdam</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.eswa.2013.06.053</doi><tpages>7</tpages></addata></record> |
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| ispartof | Expert systems with applications, 2013-12, Vol.40 (17), p.6894-6900 |
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| language | eng |
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| source | ScienceDirect Journals (5 years ago - present) |
| subjects | Adaptive algorithms Algorithms Applied sciences Artificial intelligence Computer science control theory systems Data processing. List processing. Character string processing Exact sciences and technology Expert systems Instance selection Instance-based learning algorithms Learning Memory organisation. Data processing Preserves Reduction Software State of the art Thresholds |
| title | ATISA: Adaptive Threshold-based Instance Selection Algorithm |
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