Inverted hierarchical neuro-fuzzy BSP system: a novel neuro-fuzzy model for pattern classification and rule extraction in databases
This paper introduces the Inverted Hierarchical Neuro-Fuzzy BSP System (HNFB/sup -1/), a new neuro-fuzzy model that has been specifically created for record classification and rule extraction in databases. The HNFB/sup -1/ is based on the Hierarchical Neuro-Fuzzy Binary Space Partitioning Model (HNF...
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| Veröffentlicht in: | IEEE transactions on human-machine systems 2006-03, Vol.36 (2), p.236-248 |
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| creator | Goncalves, L.B. Vellasco, M.M.B.R. Pacheco, M.A.C. Flavio Joaquim de Souza |
| description | This paper introduces the Inverted Hierarchical Neuro-Fuzzy BSP System (HNFB/sup -1/), a new neuro-fuzzy model that has been specifically created for record classification and rule extraction in databases. The HNFB/sup -1/ is based on the Hierarchical Neuro-Fuzzy Binary Space Partitioning Model (HNFB), which embodies a recursive partitioning of the input space, is able to automatically generate its own structure, and allows a greater number of inputs. The new HNFB/sup -1/ allows the extraction of knowledge in the form of interpretable fuzzy rules expressed by the following: If x is A and y is B, then input pattern belongs to class Z. For the process of rule extraction in the HNFB/sup -1/ model, two fuzzy evaluation measures were defined: 1) fuzzy accuracy and 2) fuzzy coverage. The HNFB/sup -1/ has been evaluated with different benchmark databases for the classification task: Iris Dataset, Wine Data, Pima Indians Diabetes Database, Bupa Liver Disorders, and Heart Disease. When compared with several other pattern classification models and algorithms, the HNFB/sup -1/ model has shown similar or better classification performance. Nevertheless, its performance in terms of processing time is remarkable. The HNFB/sup -1/ converged in less than one minute for all the databases described in the case study. |
| doi_str_mv | 10.1109/TSMCC.2004.843220 |
| format | Article |
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The HNFB/sup -1/ is based on the Hierarchical Neuro-Fuzzy Binary Space Partitioning Model (HNFB), which embodies a recursive partitioning of the input space, is able to automatically generate its own structure, and allows a greater number of inputs. The new HNFB/sup -1/ allows the extraction of knowledge in the form of interpretable fuzzy rules expressed by the following: If x is A and y is B, then input pattern belongs to class Z. For the process of rule extraction in the HNFB/sup -1/ model, two fuzzy evaluation measures were defined: 1) fuzzy accuracy and 2) fuzzy coverage. The HNFB/sup -1/ has been evaluated with different benchmark databases for the classification task: Iris Dataset, Wine Data, Pima Indians Diabetes Database, Bupa Liver Disorders, and Heart Disease. When compared with several other pattern classification models and algorithms, the HNFB/sup -1/ model has shown similar or better classification performance. Nevertheless, its performance in terms of processing time is remarkable. The HNFB/sup -1/ converged in less than one minute for all the databases described in the case study.</description><identifier>ISSN: 1094-6977</identifier><identifier>ISSN: 2168-2291</identifier><identifier>EISSN: 1558-2442</identifier><identifier>EISSN: 2168-2305</identifier><identifier>DOI: 10.1109/TSMCC.2004.843220</identifier><identifier>CODEN: ITCRFH</identifier><language>eng</language><publisher>New-York, NY: IEEE</publisher><subject>Applied sciences ; Artificial intelligence ; Artificial neural networks ; Binary space partitioning (BSP) ; Cardiac disease ; Classification ; Classification algorithms ; Computer science; control theory; systems ; Data mining ; Diabetes ; Exact sciences and technology ; Extraction ; Fuzzy ; Fuzzy logic ; Fuzzy neural networks ; Fuzzy set theory ; Fuzzy sets ; Iris ; Liver ; Mathematical models ; Neural networks ; neuro-fuzzy systems ; Pattern classification ; Pattern recognition. Digital image processing. Computational geometry ; rule extraction ; Studies</subject><ispartof>IEEE transactions on human-machine systems, 2006-03, Vol.36 (2), p.236-248</ispartof><rights>2006 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2006</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c417t-199369044bb89237940bec4e02e9b68b422280af33629fbe024099018bf3c5c3</citedby><cites>FETCH-LOGICAL-c417t-199369044bb89237940bec4e02e9b68b422280af33629fbe024099018bf3c5c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1624549$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1624549$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17722960$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Goncalves, L.B.</creatorcontrib><creatorcontrib>Vellasco, M.M.B.R.</creatorcontrib><creatorcontrib>Pacheco, M.A.C.</creatorcontrib><creatorcontrib>Flavio Joaquim de Souza</creatorcontrib><title>Inverted hierarchical neuro-fuzzy BSP system: a novel neuro-fuzzy model for pattern classification and rule extraction in databases</title><title>IEEE transactions on human-machine systems</title><addtitle>TSMCC</addtitle><description>This paper introduces the Inverted Hierarchical Neuro-Fuzzy BSP System (HNFB/sup -1/), a new neuro-fuzzy model that has been specifically created for record classification and rule extraction in databases. The HNFB/sup -1/ is based on the Hierarchical Neuro-Fuzzy Binary Space Partitioning Model (HNFB), which embodies a recursive partitioning of the input space, is able to automatically generate its own structure, and allows a greater number of inputs. The new HNFB/sup -1/ allows the extraction of knowledge in the form of interpretable fuzzy rules expressed by the following: If x is A and y is B, then input pattern belongs to class Z. For the process of rule extraction in the HNFB/sup -1/ model, two fuzzy evaluation measures were defined: 1) fuzzy accuracy and 2) fuzzy coverage. The HNFB/sup -1/ has been evaluated with different benchmark databases for the classification task: Iris Dataset, Wine Data, Pima Indians Diabetes Database, Bupa Liver Disorders, and Heart Disease. When compared with several other pattern classification models and algorithms, the HNFB/sup -1/ model has shown similar or better classification performance. Nevertheless, its performance in terms of processing time is remarkable. The HNFB/sup -1/ converged in less than one minute for all the databases described in the case study.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Artificial neural networks</subject><subject>Binary space partitioning (BSP)</subject><subject>Cardiac disease</subject><subject>Classification</subject><subject>Classification algorithms</subject><subject>Computer science; control theory; systems</subject><subject>Data mining</subject><subject>Diabetes</subject><subject>Exact sciences and technology</subject><subject>Extraction</subject><subject>Fuzzy</subject><subject>Fuzzy logic</subject><subject>Fuzzy neural networks</subject><subject>Fuzzy set theory</subject><subject>Fuzzy sets</subject><subject>Iris</subject><subject>Liver</subject><subject>Mathematical models</subject><subject>Neural networks</subject><subject>neuro-fuzzy systems</subject><subject>Pattern classification</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>rule extraction</subject><subject>Studies</subject><issn>1094-6977</issn><issn>2168-2291</issn><issn>1558-2442</issn><issn>2168-2305</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqNkl9LHDEUxYfSQq32A5S-hILSl1nzfya-1aWtgtKC-x4y2RuMzCZrkpGur_3izbqC6IP06V7O_d1zCSdN84ngGSFYHS-uLufzGcWYz3rOKMVvmj0iRN9Szunb2mPFW6m67n3zIecbjAnniu01f8_DHaQCS3TtIZlkr701Iwowpdi66f5-g06vfqO8yQVWJ8igEO_g-XwVl1VxMaG1KQVSQHY0OXtXnYqPAZmwRGkaAcGfkox90HxAS1PMYDLkg-adM2OGj491v1n8-L6Yn7UXv36ez79dtJaTrrREKSYV5nwYekVZpzgewHLAFNQg-4FTSntsHGOSKjdUnWOlMOkHx6ywbL852tmuU7ydIBe98tnCOJoAccqa9vWA6MR_gFgI3JEKfn0VJLJCnEkiK_rlBXoTpxTqc3UvhVKVoxUiO8immHMCp9fJr0zaaIL1Nmb9ELPexqx3Mdedw0djk2tyLplgfX5a7DpKldxyn3ecB4CnsaRc1I_wDz4nsPU</recordid><startdate>20060301</startdate><enddate>20060301</enddate><creator>Goncalves, L.B.</creator><creator>Vellasco, M.M.B.R.</creator><creator>Pacheco, M.A.C.</creator><creator>Flavio Joaquim de Souza</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope><scope>H8D</scope></search><sort><creationdate>20060301</creationdate><title>Inverted hierarchical neuro-fuzzy BSP system: a novel neuro-fuzzy model for pattern classification and rule extraction in databases</title><author>Goncalves, L.B. ; Vellasco, M.M.B.R. ; Pacheco, M.A.C. ; Flavio Joaquim de Souza</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c417t-199369044bb89237940bec4e02e9b68b422280af33629fbe024099018bf3c5c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Artificial neural networks</topic><topic>Binary space partitioning (BSP)</topic><topic>Cardiac disease</topic><topic>Classification</topic><topic>Classification algorithms</topic><topic>Computer science; control theory; systems</topic><topic>Data mining</topic><topic>Diabetes</topic><topic>Exact sciences and technology</topic><topic>Extraction</topic><topic>Fuzzy</topic><topic>Fuzzy logic</topic><topic>Fuzzy neural networks</topic><topic>Fuzzy set theory</topic><topic>Fuzzy sets</topic><topic>Iris</topic><topic>Liver</topic><topic>Mathematical models</topic><topic>Neural networks</topic><topic>neuro-fuzzy systems</topic><topic>Pattern classification</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>rule extraction</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Goncalves, L.B.</creatorcontrib><creatorcontrib>Vellasco, M.M.B.R.</creatorcontrib><creatorcontrib>Pacheco, M.A.C.</creatorcontrib><creatorcontrib>Flavio Joaquim de Souza</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering 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><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Aerospace Database</collection><jtitle>IEEE transactions on human-machine systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Goncalves, L.B.</au><au>Vellasco, M.M.B.R.</au><au>Pacheco, M.A.C.</au><au>Flavio Joaquim de Souza</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Inverted hierarchical neuro-fuzzy BSP system: a novel neuro-fuzzy model for pattern classification and rule extraction in databases</atitle><jtitle>IEEE transactions on human-machine systems</jtitle><stitle>TSMCC</stitle><date>2006-03-01</date><risdate>2006</risdate><volume>36</volume><issue>2</issue><spage>236</spage><epage>248</epage><pages>236-248</pages><issn>1094-6977</issn><issn>2168-2291</issn><eissn>1558-2442</eissn><eissn>2168-2305</eissn><coden>ITCRFH</coden><abstract>This paper introduces the Inverted Hierarchical Neuro-Fuzzy BSP System (HNFB/sup -1/), a new neuro-fuzzy model that has been specifically created for record classification and rule extraction in databases. The HNFB/sup -1/ is based on the Hierarchical Neuro-Fuzzy Binary Space Partitioning Model (HNFB), which embodies a recursive partitioning of the input space, is able to automatically generate its own structure, and allows a greater number of inputs. The new HNFB/sup -1/ allows the extraction of knowledge in the form of interpretable fuzzy rules expressed by the following: If x is A and y is B, then input pattern belongs to class Z. For the process of rule extraction in the HNFB/sup -1/ model, two fuzzy evaluation measures were defined: 1) fuzzy accuracy and 2) fuzzy coverage. The HNFB/sup -1/ has been evaluated with different benchmark databases for the classification task: Iris Dataset, Wine Data, Pima Indians Diabetes Database, Bupa Liver Disorders, and Heart Disease. When compared with several other pattern classification models and algorithms, the HNFB/sup -1/ model has shown similar or better classification performance. Nevertheless, its performance in terms of processing time is remarkable. The HNFB/sup -1/ converged in less than one minute for all the databases described in the case study.</abstract><cop>New-York, NY</cop><pub>IEEE</pub><doi>10.1109/TSMCC.2004.843220</doi><tpages>13</tpages></addata></record> |
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| ispartof | IEEE transactions on human-machine systems, 2006-03, Vol.36 (2), p.236-248 |
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| source | IEEE Electronic Library (IEL) |
| subjects | Applied sciences Artificial intelligence Artificial neural networks Binary space partitioning (BSP) Cardiac disease Classification Classification algorithms Computer science control theory systems Data mining Diabetes Exact sciences and technology Extraction Fuzzy Fuzzy logic Fuzzy neural networks Fuzzy set theory Fuzzy sets Iris Liver Mathematical models Neural networks neuro-fuzzy systems Pattern classification Pattern recognition. Digital image processing. Computational geometry rule extraction Studies |
| title | Inverted hierarchical neuro-fuzzy BSP system: a novel neuro-fuzzy model for pattern classification and rule extraction in databases |
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