A classification scheme for applications with ambiguous data
We propose a scheme for pattern classifications in applications which include ambiguous data, that is, where pattern occupy overlapping areas in the feature space. Such situations frequently occur with noisy data and/or where some features are unknown. We demonstrate that it is advantageous to first...
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| container_end_page | 301 vol.6 |
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| creator | Trappenberg, T.P. Back, A.D. |
| description | We propose a scheme for pattern classifications in applications which include ambiguous data, that is, where pattern occupy overlapping areas in the feature space. Such situations frequently occur with noisy data and/or where some features are unknown. We demonstrate that it is advantageous to first detect those ambiguous areas with the help of training data and then to re-classify those data in these areas as ambiguous before making class predictions on test sets. This scheme is demonstrated with a simple example and benchmarked on two real world applications. |
| doi_str_mv | 10.1109/IJCNN.2000.859412 |
| format | Conference Proceeding |
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Such situations frequently occur with noisy data and/or where some features are unknown. We demonstrate that it is advantageous to first detect those ambiguous areas with the help of training data and then to re-classify those data in these areas as ambiguous before making class predictions on test sets. This scheme is demonstrated with a simple example and benchmarked on two real world applications.</description><identifier>ISSN: 1098-7576</identifier><identifier>ISBN: 9780769506197</identifier><identifier>ISBN: 0769506194</identifier><identifier>EISSN: 1558-3902</identifier><identifier>DOI: 10.1109/IJCNN.2000.859412</identifier><language>eng</language><publisher>IEEE</publisher><subject>Artificial neural networks ; Bayesian methods ; Benchmark testing ; Data mining ; Linear discriminant analysis ; Machine learning algorithms ; Neuroscience ; Pattern recognition ; Psychology ; Training data</subject><ispartof>Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. 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This scheme is demonstrated with a simple example and benchmarked on two real world applications.</description><subject>Artificial neural networks</subject><subject>Bayesian methods</subject><subject>Benchmark testing</subject><subject>Data mining</subject><subject>Linear discriminant analysis</subject><subject>Machine learning algorithms</subject><subject>Neuroscience</subject><subject>Pattern recognition</subject><subject>Psychology</subject><subject>Training data</subject><issn>1098-7576</issn><issn>1558-3902</issn><isbn>9780769506197</isbn><isbn>0769506194</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2000</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotj1tLw0AUhBcvYKn5Afq0fyDxnOwdfCnBS6XUF30uZzcbu9KakE0R_72F9mlghm-YYewOoUIE97B8a9brqgaAyionsb5gM1TKlsJBfckKZywY7RRodObqmIGzpVFG37Ai5-8jhyCUrnHGHhc87Cjn1KVAU-p_eA7buI-860dOw7A725n_pmnLae_T16E_ZN7SRLfsuqNdjsVZ5-zz-emjeS1X7y_LZrEqE4KcSitt10aBTkBUWnvSRpJ3QoUWvfUSlY9BBBLBShliraXwprOEQdvjTCfm7P7Um2KMm2FMexr_Nqfr4h8qiEqk</recordid><startdate>2000</startdate><enddate>2000</enddate><creator>Trappenberg, T.P.</creator><creator>Back, A.D.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>2000</creationdate><title>A classification scheme for applications with ambiguous data</title><author>Trappenberg, T.P. ; Back, A.D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i104t-848fde31930e566ba674ab935cd1b8b415bec3ca3c844ce2643b7f8a1c6835693</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2000</creationdate><topic>Artificial neural networks</topic><topic>Bayesian methods</topic><topic>Benchmark testing</topic><topic>Data mining</topic><topic>Linear discriminant analysis</topic><topic>Machine learning algorithms</topic><topic>Neuroscience</topic><topic>Pattern recognition</topic><topic>Psychology</topic><topic>Training data</topic><toplevel>online_resources</toplevel><creatorcontrib>Trappenberg, T.P.</creatorcontrib><creatorcontrib>Back, A.D.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Trappenberg, T.P.</au><au>Back, A.D.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A classification scheme for applications with ambiguous data</atitle><btitle>Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium</btitle><stitle>IJCNN</stitle><date>2000</date><risdate>2000</risdate><volume>6</volume><spage>296</spage><epage>301 vol.6</epage><pages>296-301 vol.6</pages><issn>1098-7576</issn><eissn>1558-3902</eissn><isbn>9780769506197</isbn><isbn>0769506194</isbn><abstract>We propose a scheme for pattern classifications in applications which include ambiguous data, that is, where pattern occupy overlapping areas in the feature space. Such situations frequently occur with noisy data and/or where some features are unknown. We demonstrate that it is advantageous to first detect those ambiguous areas with the help of training data and then to re-classify those data in these areas as ambiguous before making class predictions on test sets. This scheme is demonstrated with a simple example and benchmarked on two real world applications.</abstract><pub>IEEE</pub><doi>10.1109/IJCNN.2000.859412</doi></addata></record> |
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| ispartof | Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000, Vol.6, p.296-301 vol.6 |
| issn | 1098-7576 1558-3902 |
| language | eng |
| recordid | cdi_ieee_primary_859412 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Bayesian methods Benchmark testing Data mining Linear discriminant analysis Machine learning algorithms Neuroscience Pattern recognition Psychology Training data |
| title | A classification scheme for applications with ambiguous data |
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