A Stigmergy Based Approach to Data Mining
In this paper, we report on the use of ant systems in the data mining field capable of extracting comprehensible classifiers from data. The ant system used is a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \use...
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| creator | De Backer, Manu Haesen, Raf Martens, David Baesens, Bart |
| description | In this paper, we report on the use of ant systems in the data mining field capable of extracting comprehensible classifiers from data. The ant system used is a \documentclass[12pt]{minimal}
\usepackage{amsmath}
\usepackage{wasysym}
\usepackage{amsfonts}
\usepackage{amssymb}
\usepackage{amsbsy}
\usepackage{mathrsfs}
\usepackage{upgreek}
\setlength{\oddsidemargin}{-69pt}
\begin{document}${\mathcal MAX}-{\mathcal MIN}$\end{document} ant system which differs from the originally proposed ant systems in its ability to explore bigger parts of the solution space, yielding better performing rules. Furthermore, we are able to include intervals in the rules resulting in less and shorter rules. Our experiments show a significant improvement of the performance both in accuracy and comprehensibility, compared to previous data mining techniques based on ant systems and other state-of-the-art classification techniques. |
| doi_str_mv | 10.1007/11589990_123 |
| format | Book Chapter |
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\begin{document}${\mathcal MAX}-{\mathcal MIN}$\end{document} ant system which differs from the originally proposed ant systems in its ability to explore bigger parts of the solution space, yielding better performing rules. Furthermore, we are able to include intervals in the rules resulting in less and shorter rules. Our experiments show a significant improvement of the performance both in accuracy and comprehensibility, compared to previous data mining techniques based on ant systems and other state-of-the-art classification techniques.</description><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>Memory organisation. Data processing</subject><subject>Software</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>3540304622</isbn><isbn>9783540304623</isbn><isbn>3540316523</isbn><isbn>9783540316527</isbn><fulltext>true</fulltext><rsrctype>book_chapter</rsrctype><creationdate>2005</creationdate><recordtype>book_chapter</recordtype><recordid>eNpNkDtPwzAUhc1LIi1s_IAsDCAFfH3tOB7b8pSKGIA5unbsEGiTKM7Sf0-kdmA6RzqfzvAxdgX8DjjX9wCqMMbwEgQesRkqyRFyJfCYJZADZIjSnBwGLnMhTlkyNZEZLfGczWL84ZwLbUTCbhbpx9jUWz_Uu3RJ0Vfpou-Hjtx3OnbpA42UvjVt09YX7CzQJvrLQ87Z19Pj5-olW78_v64W68whhzFzSpIVUjmjbU7KohVoco5Y2YICLzxKCVQVWgcRhKuU8qEgF1RVBTUhOGfX-9-eoqNNGKh1TSz7odnSsCtBo1Qgiom73XNxmtraD6Xtut9YwiRm0lT-14R_dZFTkg</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>De Backer, Manu</creator><creator>Haesen, Raf</creator><creator>Martens, David</creator><creator>Baesens, Bart</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>IQODW</scope></search><sort><creationdate>2005</creationdate><title>A Stigmergy Based Approach to Data Mining</title><author>De Backer, Manu ; Haesen, Raf ; Martens, David ; Baesens, Bart</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c301t-c54ab245c97b6a5b3b2396033db8af08e3441ad877f2f2cd55ef8acf5ddf5b8a3</frbrgroupid><rsrctype>book_chapters</rsrctype><prefilter>book_chapters</prefilter><language>eng</language><creationdate>2005</creationdate><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>Memory organisation. Data processing</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>De Backer, Manu</creatorcontrib><creatorcontrib>Haesen, Raf</creatorcontrib><creatorcontrib>Martens, David</creatorcontrib><creatorcontrib>Baesens, Bart</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>De Backer, Manu</au><au>Haesen, Raf</au><au>Martens, David</au><au>Baesens, Bart</au><au>Jarvis, Ray</au><au>Zhang, Shichao</au><format>book</format><genre>bookitem</genre><ristype>CHAP</ristype><atitle>A Stigmergy Based Approach to Data Mining</atitle><btitle>AI 2005: Advances in Artificial Intelligence</btitle><seriestitle>Lecture Notes in Computer Science</seriestitle><date>2005</date><risdate>2005</risdate><spage>975</spage><epage>978</epage><pages>975-978</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>3540304622</isbn><isbn>9783540304623</isbn><eisbn>3540316523</eisbn><eisbn>9783540316527</eisbn><abstract>In this paper, we report on the use of ant systems in the data mining field capable of extracting comprehensible classifiers from data. The ant system used is a \documentclass[12pt]{minimal}
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\begin{document}${\mathcal MAX}-{\mathcal MIN}$\end{document} ant system which differs from the originally proposed ant systems in its ability to explore bigger parts of the solution space, yielding better performing rules. Furthermore, we are able to include intervals in the rules resulting in less and shorter rules. Our experiments show a significant improvement of the performance both in accuracy and comprehensibility, compared to previous data mining techniques based on ant systems and other state-of-the-art classification techniques.</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/11589990_123</doi><tpages>4</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | AI 2005: Advances in Artificial Intelligence, 2005, p.975-978 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_17345128 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Data processing. List processing. Character string processing Exact sciences and technology Memory organisation. Data processing Software |
| title | A Stigmergy Based Approach to Data Mining |
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