A rough sets & genetic based approach for rule induction
Automated knowledge acquisition is an important research area in developing expert systems. For this purpose, several methods of inductive learning have been proposed, such as decision tree, fuzzy set, Dempster-Shafer theory of evidence. However, most of the approaches require prior knowledge. Rough...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 4303 Vol.5 |
|---|---|
| container_issue | |
| container_start_page | 4300 |
| container_title | |
| container_volume | 5 |
| creator | Binbin Qu Yansheng Lu |
| description | Automated knowledge acquisition is an important research area in developing expert systems. For this purpose, several methods of inductive learning have been proposed, such as decision tree, fuzzy set, Dempster-Shafer theory of evidence. However, most of the approaches require prior knowledge. Rough sets theory is a new approach to decision making in the presence of uncertainty and vagueness, when coupled with genetic algorithms, a rule induction engine that is able to induce rules efficiently. In this paper, we use discernibility matrix to find the attribute core. Then a steady-state genetic algorithms is applied to get relative reduction, finally, rough sets theory is used to remove redundant condition attribute values to get rules. The experimental results show that the proposed method induces maximal generalized rules efficiently. |
| doi_str_mv | 10.1109/WCICA.2004.1342323 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_1342323</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>1342323</ieee_id><sourcerecordid>1342323</sourcerecordid><originalsourceid>FETCH-ieee_primary_13423233</originalsourceid><addsrcrecordid>eNp9js0KgkAURi9EUJQvUJu7apfNX6hLkaL2QUuZ9KoT5siMLnr7XLju48BZnM0HsOMs5Jwlp2d2z9JQMKZCLpWQQi4gSKKYTchYRFKsIPD-zabJ5KyYWkOcorNj3aCnweMBa-poMAW-tKcSdd87q4sGK-vQjS2h6cqxGIzttrCsdOspmL2B_fXyyG5HQ0R578xHu28-_5D_6w8JFjXi</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>A rough sets & genetic based approach for rule induction</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Binbin Qu ; Yansheng Lu</creator><creatorcontrib>Binbin Qu ; Yansheng Lu</creatorcontrib><description>Automated knowledge acquisition is an important research area in developing expert systems. For this purpose, several methods of inductive learning have been proposed, such as decision tree, fuzzy set, Dempster-Shafer theory of evidence. However, most of the approaches require prior knowledge. Rough sets theory is a new approach to decision making in the presence of uncertainty and vagueness, when coupled with genetic algorithms, a rule induction engine that is able to induce rules efficiently. In this paper, we use discernibility matrix to find the attribute core. Then a steady-state genetic algorithms is applied to get relative reduction, finally, rough sets theory is used to remove redundant condition attribute values to get rules. The experimental results show that the proposed method induces maximal generalized rules efficiently.</description><identifier>ISBN: 9780780382732</identifier><identifier>ISBN: 0780382730</identifier><identifier>DOI: 10.1109/WCICA.2004.1342323</identifier><language>eng</language><publisher>IEEE</publisher><subject>Decision making ; Decision trees ; Engines ; Expert systems ; Fuzzy set theory ; Genetic algorithms ; Knowledge acquisition ; Rough sets ; Steady-state ; Uncertainty</subject><ispartof>Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788), 2004, Vol.5, p.4300-4303 Vol.5</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/1342323$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>310,311,781,785,790,791,2059,4051,4052,27929,54924</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1342323$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Binbin Qu</creatorcontrib><creatorcontrib>Yansheng Lu</creatorcontrib><title>A rough sets & genetic based approach for rule induction</title><title>Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788)</title><addtitle>WCICA</addtitle><description>Automated knowledge acquisition is an important research area in developing expert systems. For this purpose, several methods of inductive learning have been proposed, such as decision tree, fuzzy set, Dempster-Shafer theory of evidence. However, most of the approaches require prior knowledge. Rough sets theory is a new approach to decision making in the presence of uncertainty and vagueness, when coupled with genetic algorithms, a rule induction engine that is able to induce rules efficiently. In this paper, we use discernibility matrix to find the attribute core. Then a steady-state genetic algorithms is applied to get relative reduction, finally, rough sets theory is used to remove redundant condition attribute values to get rules. The experimental results show that the proposed method induces maximal generalized rules efficiently.</description><subject>Decision making</subject><subject>Decision trees</subject><subject>Engines</subject><subject>Expert systems</subject><subject>Fuzzy set theory</subject><subject>Genetic algorithms</subject><subject>Knowledge acquisition</subject><subject>Rough sets</subject><subject>Steady-state</subject><subject>Uncertainty</subject><isbn>9780780382732</isbn><isbn>0780382730</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2004</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNp9js0KgkAURi9EUJQvUJu7apfNX6hLkaL2QUuZ9KoT5siMLnr7XLju48BZnM0HsOMs5Jwlp2d2z9JQMKZCLpWQQi4gSKKYTchYRFKsIPD-zabJ5KyYWkOcorNj3aCnweMBa-poMAW-tKcSdd87q4sGK-vQjS2h6cqxGIzttrCsdOspmL2B_fXyyG5HQ0R578xHu28-_5D_6w8JFjXi</recordid><startdate>2004</startdate><enddate>2004</enddate><creator>Binbin Qu</creator><creator>Yansheng Lu</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2004</creationdate><title>A rough sets & genetic based approach for rule induction</title><author>Binbin Qu ; Yansheng Lu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_13423233</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2004</creationdate><topic>Decision making</topic><topic>Decision trees</topic><topic>Engines</topic><topic>Expert systems</topic><topic>Fuzzy set theory</topic><topic>Genetic algorithms</topic><topic>Knowledge acquisition</topic><topic>Rough sets</topic><topic>Steady-state</topic><topic>Uncertainty</topic><toplevel>online_resources</toplevel><creatorcontrib>Binbin Qu</creatorcontrib><creatorcontrib>Yansheng Lu</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 Electronic Library Online</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>Binbin Qu</au><au>Yansheng Lu</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A rough sets & genetic based approach for rule induction</atitle><btitle>Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788)</btitle><stitle>WCICA</stitle><date>2004</date><risdate>2004</risdate><volume>5</volume><spage>4300</spage><epage>4303 Vol.5</epage><pages>4300-4303 Vol.5</pages><isbn>9780780382732</isbn><isbn>0780382730</isbn><abstract>Automated knowledge acquisition is an important research area in developing expert systems. For this purpose, several methods of inductive learning have been proposed, such as decision tree, fuzzy set, Dempster-Shafer theory of evidence. However, most of the approaches require prior knowledge. Rough sets theory is a new approach to decision making in the presence of uncertainty and vagueness, when coupled with genetic algorithms, a rule induction engine that is able to induce rules efficiently. In this paper, we use discernibility matrix to find the attribute core. Then a steady-state genetic algorithms is applied to get relative reduction, finally, rough sets theory is used to remove redundant condition attribute values to get rules. The experimental results show that the proposed method induces maximal generalized rules efficiently.</abstract><pub>IEEE</pub><doi>10.1109/WCICA.2004.1342323</doi></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISBN: 9780780382732 |
| ispartof | Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788), 2004, Vol.5, p.4300-4303 Vol.5 |
| issn | |
| language | eng |
| recordid | cdi_ieee_primary_1342323 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Decision making Decision trees Engines Expert systems Fuzzy set theory Genetic algorithms Knowledge acquisition Rough sets Steady-state Uncertainty |
| title | A rough sets & genetic based approach for rule induction |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-16T21%3A55%3A42IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=A%20rough%20sets%20&%20genetic%20based%20approach%20for%20rule%20induction&rft.btitle=Fifth%20World%20Congress%20on%20Intelligent%20Control%20and%20Automation%20(IEEE%20Cat.%20No.04EX788)&rft.au=Binbin%20Qu&rft.date=2004&rft.volume=5&rft.spage=4300&rft.epage=4303%20Vol.5&rft.pages=4300-4303%20Vol.5&rft.isbn=9780780382732&rft.isbn_list=0780382730&rft_id=info:doi/10.1109/WCICA.2004.1342323&rft_dat=%3Cieee_6IE%3E1342323%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=1342323&rfr_iscdi=true |