Data Reduction for Instance-Based Learning Using Entropy-Based Partitioning
Instance-based learning methods such as the nearest neighbor classifier have proven to perform well in pattern classification in several fields. Despite their high classification accuracy, they suffer from a high storage requirement, computational cost, and sensitivity to noise. In this paper, we pr...
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| creator | Son, Seung-Hyun Kim, Jae-Yearn |
| description | Instance-based learning methods such as the nearest neighbor classifier have proven to perform well in pattern classification in several fields. Despite their high classification accuracy, they suffer from a high storage requirement, computational cost, and sensitivity to noise. In this paper, we present a data reduction method for instance-based learning, based on entropy-based partitioning and representative instances. Experimental results show that the new algorithm achieves a high data reduction rate as well as classification accuracy. |
| doi_str_mv | 10.1007/11751595_63 |
| format | Conference Proceeding |
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J. Kenneth ; Mun, Youngsong</contributor><creatorcontrib>Son, Seung-Hyun ; Kim, Jae-Yearn ; Choo, Hyunseung ; Gervasi, Osvaldo ; Taniar, David ; Gavrilova, Marina ; Laganá, Antonio ; Kumar, Vipin ; Tan, C. J. Kenneth ; Mun, Youngsong</creatorcontrib><description>Instance-based learning methods such as the nearest neighbor classifier have proven to perform well in pattern classification in several fields. Despite their high classification accuracy, they suffer from a high storage requirement, computational cost, and sensitivity to noise. In this paper, we present a data reduction method for instance-based learning, based on entropy-based partitioning and representative instances. Experimental results show that the new algorithm achieves a high data reduction rate as well as classification accuracy.</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 9783540340751</identifier><identifier>ISBN: 3540340750</identifier><identifier>ISBN: 354034070X</identifier><identifier>ISBN: 9783540340706</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 3540340769</identifier><identifier>EISBN: 9783540340768</identifier><identifier>DOI: 10.1007/11751595_63</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithmics. Computability. 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J. Kenneth</contributor><contributor>Mun, Youngsong</contributor><creatorcontrib>Son, Seung-Hyun</creatorcontrib><creatorcontrib>Kim, Jae-Yearn</creatorcontrib><title>Data Reduction for Instance-Based Learning Using Entropy-Based Partitioning</title><title>Computational Science and Its Applications - ICCSA 2006</title><description>Instance-based learning methods such as the nearest neighbor classifier have proven to perform well in pattern classification in several fields. Despite their high classification accuracy, they suffer from a high storage requirement, computational cost, and sensitivity to noise. In this paper, we present a data reduction method for instance-based learning, based on entropy-based partitioning and representative instances. Experimental results show that the new algorithm achieves a high data reduction rate as well as classification accuracy.</description><subject>Algorithmics. Computability. Computer arithmetics</subject><subject>Applied sciences</subject><subject>Computer science; control theory; systems</subject><subject>Data Reduction</subject><subject>Data Reduction Method</subject><subject>Euclidean Distance Measure</subject><subject>Exact sciences and technology</subject><subject>Irrelevant Attribute</subject><subject>Representative Instance</subject><subject>Theoretical computing</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540340751</isbn><isbn>3540340750</isbn><isbn>354034070X</isbn><isbn>9783540340706</isbn><isbn>3540340769</isbn><isbn>9783540340768</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpNkDtPAzEQhM1LIoRU_IFrKCgOvPb5VUIIEBEJhEht7Tl2dDx8J_so8u-5KEFii93imxmthpALoNdAqboBUAKEEVbyA3LGRUV5RZU0h2QEEqDkvDJHZGKU_mMCjsmIcspKoyp-SiY5f9BhOEjN9Yg832OPxZtf_bi-aWMR2lTMY-4xOl_eYfarYuExxSaui2Xe7lnsU9tt9vAVU99snQM6JycBv7Kf7O-YLB9m79OncvHyOJ_eLsqOgenLIJXjQq9kLYykKqCXtYfgJdPOS6y4516jr1ArzbRyjklTGwNMBokgPB-Ty11uh9nhV0jDs022XWq-MW0sGCO1YHrQXe10eUBx7ZOt2_YzW6B226b91yb_BS-kYVk</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Son, Seung-Hyun</creator><creator>Kim, Jae-Yearn</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2006</creationdate><title>Data Reduction for Instance-Based Learning Using Entropy-Based Partitioning</title><author>Son, Seung-Hyun ; Kim, Jae-Yearn</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p219t-f67c358d6b59607fae6be1fe628ce6a43e3e8ae4a878287cc269b99126f6a15e3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Algorithmics. Computability. Computer arithmetics</topic><topic>Applied sciences</topic><topic>Computer science; control theory; systems</topic><topic>Data Reduction</topic><topic>Data Reduction Method</topic><topic>Euclidean Distance Measure</topic><topic>Exact sciences and technology</topic><topic>Irrelevant Attribute</topic><topic>Representative Instance</topic><topic>Theoretical computing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Son, Seung-Hyun</creatorcontrib><creatorcontrib>Kim, Jae-Yearn</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Son, Seung-Hyun</au><au>Kim, Jae-Yearn</au><au>Choo, Hyunseung</au><au>Gervasi, Osvaldo</au><au>Taniar, David</au><au>Gavrilova, Marina</au><au>Laganá, Antonio</au><au>Kumar, Vipin</au><au>Tan, C. J. Kenneth</au><au>Mun, Youngsong</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Data Reduction for Instance-Based Learning Using Entropy-Based Partitioning</atitle><btitle>Computational Science and Its Applications - ICCSA 2006</btitle><date>2006</date><risdate>2006</risdate><spage>590</spage><epage>599</epage><pages>590-599</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540340751</isbn><isbn>3540340750</isbn><isbn>354034070X</isbn><isbn>9783540340706</isbn><eisbn>3540340769</eisbn><eisbn>9783540340768</eisbn><abstract>Instance-based learning methods such as the nearest neighbor classifier have proven to perform well in pattern classification in several fields. Despite their high classification accuracy, they suffer from a high storage requirement, computational cost, and sensitivity to noise. In this paper, we present a data reduction method for instance-based learning, based on entropy-based partitioning and representative instances. Experimental results show that the new algorithm achieves a high data reduction rate as well as classification accuracy.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11751595_63</doi><tpages>10</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Computational Science and Its Applications - ICCSA 2006, 2006, p.590-599 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_19968528 |
| source | Springer Books |
| subjects | Algorithmics. Computability. Computer arithmetics Applied sciences Computer science control theory systems Data Reduction Data Reduction Method Euclidean Distance Measure Exact sciences and technology Irrelevant Attribute Representative Instance Theoretical computing |
| title | Data Reduction for Instance-Based Learning Using Entropy-Based Partitioning |
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