Genetically optimized fuzzy decision trees
In this study, we are concerned with genetically optimized fuzzy decision trees (G-DTs). Decision trees are fundamental architectures of machine learning, pattern recognition, and system modeling. Starting with the generic decision tree with discrete or interval-valued attributes, we develop its fuz...
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| Veröffentlicht in: | IEEE transactions on cybernetics 2005-06, Vol.35 (3), p.633-641 |
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| creator | Pedrycz, W. Sosnowski, Z.A. |
| description | In this study, we are concerned with genetically optimized fuzzy decision trees (G-DTs). Decision trees are fundamental architectures of machine learning, pattern recognition, and system modeling. Starting with the generic decision tree with discrete or interval-valued attributes, we develop its fuzzy set-based generalization. In this generalized structure we admit the values of the attributes that are represented by some membership functions. Such fuzzy decision trees are constructed in the setting of genetic optimization. The underlying genetic algorithm optimizes the parameters of the fuzzy sets associated with the individual nodes where they play a role of fuzzy "switches" by distributing a flow of processing completed within the tree. We discuss various forms of the fitness function that help capture the essence of the problem at hand (that could be either of classification nature when dealing with discrete outputs or regression-like when handling a continuous output variable). We quantify a nature of the generalization of the tree by studying an optimally adjusted spreads of the membership functions located at the nodes of the decision tree. A series of experiments exploiting synthetic and machine learning data is used to illustrate the performance of the G-DTs. |
| doi_str_mv | 10.1109/TSMCB.2005.843975 |
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Decision trees are fundamental architectures of machine learning, pattern recognition, and system modeling. Starting with the generic decision tree with discrete or interval-valued attributes, we develop its fuzzy set-based generalization. In this generalized structure we admit the values of the attributes that are represented by some membership functions. Such fuzzy decision trees are constructed in the setting of genetic optimization. The underlying genetic algorithm optimizes the parameters of the fuzzy sets associated with the individual nodes where they play a role of fuzzy "switches" by distributing a flow of processing completed within the tree. We discuss various forms of the fitness function that help capture the essence of the problem at hand (that could be either of classification nature when dealing with discrete outputs or regression-like when handling a continuous output variable). We quantify a nature of the generalization of the tree by studying an optimally adjusted spreads of the membership functions located at the nodes of the decision tree. A series of experiments exploiting synthetic and machine learning data is used to illustrate the performance of the G-DTs.</description><identifier>ISSN: 1083-4419</identifier><identifier>ISSN: 2168-2267</identifier><identifier>EISSN: 1941-0492</identifier><identifier>EISSN: 2168-2275</identifier><identifier>DOI: 10.1109/TSMCB.2005.843975</identifier><identifier>PMID: 15971931</identifier><identifier>CODEN: ITSCFI</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Algorithm design and analysis ; Algorithms ; Artificial Intelligence ; Cluster Analysis ; Competitive intelligence ; Computational intelligence ; Computer Simulation ; Cybernetics ; Decision Support Techniques ; Decision trees ; Fuzzy ; fuzzy decision trees ; Fuzzy Logic ; Fuzzy set theory ; Fuzzy sets ; genetic algorithm (GA) ; Genetic algorithms ; Image Enhancement - methods ; Image Interpretation, Computer-Assisted - methods ; Information Storage and Retrieval - methods ; Learning systems ; Machine learning ; Mathematical models ; Modeling ; Models, Biological ; Models, Statistical ; Numerical Analysis, Computer-Assisted ; Pattern recognition ; Pattern Recognition, Automated - methods ; propagation mechanisms ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; Studies ; Topology ; Trees ; triangular norms and co-norms ; two-stage design</subject><ispartof>IEEE transactions on cybernetics, 2005-06, Vol.35 (3), p.633-641</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2005</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c410t-683ed506feadcae567f7dcf22b8a4d3634a53b20b87533b1f478713b61a46fd13</citedby><cites>FETCH-LOGICAL-c410t-683ed506feadcae567f7dcf22b8a4d3634a53b20b87533b1f478713b61a46fd13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1430847$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27923,27924,54757</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1430847$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/15971931$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Pedrycz, W.</creatorcontrib><creatorcontrib>Sosnowski, Z.A.</creatorcontrib><title>Genetically optimized fuzzy decision trees</title><title>IEEE transactions on cybernetics</title><addtitle>TSMCB</addtitle><addtitle>IEEE Trans Syst Man Cybern B Cybern</addtitle><description>In this study, we are concerned with genetically optimized fuzzy decision trees (G-DTs). Decision trees are fundamental architectures of machine learning, pattern recognition, and system modeling. Starting with the generic decision tree with discrete or interval-valued attributes, we develop its fuzzy set-based generalization. In this generalized structure we admit the values of the attributes that are represented by some membership functions. Such fuzzy decision trees are constructed in the setting of genetic optimization. The underlying genetic algorithm optimizes the parameters of the fuzzy sets associated with the individual nodes where they play a role of fuzzy "switches" by distributing a flow of processing completed within the tree. We discuss various forms of the fitness function that help capture the essence of the problem at hand (that could be either of classification nature when dealing with discrete outputs or regression-like when handling a continuous output variable). We quantify a nature of the generalization of the tree by studying an optimally adjusted spreads of the membership functions located at the nodes of the decision tree. A series of experiments exploiting synthetic and machine learning data is used to illustrate the performance of the G-DTs.</description><subject>Algorithm design and analysis</subject><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Cluster Analysis</subject><subject>Competitive intelligence</subject><subject>Computational intelligence</subject><subject>Computer Simulation</subject><subject>Cybernetics</subject><subject>Decision Support Techniques</subject><subject>Decision trees</subject><subject>Fuzzy</subject><subject>fuzzy decision trees</subject><subject>Fuzzy Logic</subject><subject>Fuzzy set theory</subject><subject>Fuzzy sets</subject><subject>genetic algorithm (GA)</subject><subject>Genetic algorithms</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>Information Storage and Retrieval - methods</subject><subject>Learning systems</subject><subject>Machine learning</subject><subject>Mathematical models</subject><subject>Modeling</subject><subject>Models, Biological</subject><subject>Models, Statistical</subject><subject>Numerical Analysis, Computer-Assisted</subject><subject>Pattern recognition</subject><subject>Pattern Recognition, Automated - methods</subject><subject>propagation mechanisms</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Studies</subject><subject>Topology</subject><subject>Trees</subject><subject>triangular norms and co-norms</subject><subject>two-stage design</subject><issn>1083-4419</issn><issn>2168-2267</issn><issn>1941-0492</issn><issn>2168-2275</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqFkU1Lw0AQhhdRbK3-ABGkeBARUmey30ctfkHFg3peNskEImlTs8mh_fWutiB40NMMzPO-MDyMHSNMEMFevb48TW8mKYCcGMGtljtsiFZgAsKmu3EHwxMh0A7YQQjvAGDB6n02QGk1Wo5DdnlPC-qq3Nf1atwsu2perakYl_16vRoXlFehahbjriUKh2yv9HWgo-0csbe729fpQzJ7vn-cXs-SXCB0iTKcCgmqJF_knqTSpS7yMk0z40XBFRde8iyFzGjJeYal0EYjzxR6ocoC-Yidb3qXbfPRU-jcvAo51bVfUNMHlxpjuY1F_4PA4_Mighd_gqg0SsBUmoie_ULfm75dxH-dUVppCdZGCDdQ3jYhtFS6ZVvNfbtyCO7LjPs2477MuI2ZmDndFvfZnIqfxFZFBE42QEVEP2fBwQjNPwGpapAP</recordid><startdate>20050601</startdate><enddate>20050601</enddate><creator>Pedrycz, W.</creator><creator>Sosnowski, Z.A.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20050601</creationdate><title>Genetically optimized fuzzy decision trees</title><author>Pedrycz, W. ; Sosnowski, Z.A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c410t-683ed506feadcae567f7dcf22b8a4d3634a53b20b87533b1f478713b61a46fd13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Algorithm design and analysis</topic><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Cluster Analysis</topic><topic>Competitive intelligence</topic><topic>Computational intelligence</topic><topic>Computer Simulation</topic><topic>Cybernetics</topic><topic>Decision Support Techniques</topic><topic>Decision trees</topic><topic>Fuzzy</topic><topic>fuzzy decision trees</topic><topic>Fuzzy Logic</topic><topic>Fuzzy set theory</topic><topic>Fuzzy sets</topic><topic>genetic algorithm (GA)</topic><topic>Genetic algorithms</topic><topic>Image Enhancement - methods</topic><topic>Image Interpretation, Computer-Assisted - methods</topic><topic>Information Storage and Retrieval - methods</topic><topic>Learning systems</topic><topic>Machine learning</topic><topic>Mathematical models</topic><topic>Modeling</topic><topic>Models, Biological</topic><topic>Models, Statistical</topic><topic>Numerical Analysis, Computer-Assisted</topic><topic>Pattern recognition</topic><topic>Pattern Recognition, Automated - methods</topic><topic>propagation mechanisms</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><topic>Signal Processing, Computer-Assisted</topic><topic>Studies</topic><topic>Topology</topic><topic>Trees</topic><topic>triangular norms and co-norms</topic><topic>two-stage design</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pedrycz, W.</creatorcontrib><creatorcontrib>Sosnowski, Z.A.</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>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</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>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace 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><jtitle>IEEE transactions on cybernetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Pedrycz, W.</au><au>Sosnowski, Z.A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Genetically optimized fuzzy decision trees</atitle><jtitle>IEEE transactions on cybernetics</jtitle><stitle>TSMCB</stitle><addtitle>IEEE Trans Syst Man Cybern B Cybern</addtitle><date>2005-06-01</date><risdate>2005</risdate><volume>35</volume><issue>3</issue><spage>633</spage><epage>641</epage><pages>633-641</pages><issn>1083-4419</issn><issn>2168-2267</issn><eissn>1941-0492</eissn><eissn>2168-2275</eissn><coden>ITSCFI</coden><abstract>In this study, we are concerned with genetically optimized fuzzy decision trees (G-DTs). Decision trees are fundamental architectures of machine learning, pattern recognition, and system modeling. Starting with the generic decision tree with discrete or interval-valued attributes, we develop its fuzzy set-based generalization. In this generalized structure we admit the values of the attributes that are represented by some membership functions. Such fuzzy decision trees are constructed in the setting of genetic optimization. The underlying genetic algorithm optimizes the parameters of the fuzzy sets associated with the individual nodes where they play a role of fuzzy "switches" by distributing a flow of processing completed within the tree. We discuss various forms of the fitness function that help capture the essence of the problem at hand (that could be either of classification nature when dealing with discrete outputs or regression-like when handling a continuous output variable). We quantify a nature of the generalization of the tree by studying an optimally adjusted spreads of the membership functions located at the nodes of the decision tree. A series of experiments exploiting synthetic and machine learning data is used to illustrate the performance of the G-DTs.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>15971931</pmid><doi>10.1109/TSMCB.2005.843975</doi><tpages>9</tpages></addata></record> |
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| subjects | Algorithm design and analysis Algorithms Artificial Intelligence Cluster Analysis Competitive intelligence Computational intelligence Computer Simulation Cybernetics Decision Support Techniques Decision trees Fuzzy fuzzy decision trees Fuzzy Logic Fuzzy set theory Fuzzy sets genetic algorithm (GA) Genetic algorithms Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Information Storage and Retrieval - methods Learning systems Machine learning Mathematical models Modeling Models, Biological Models, Statistical Numerical Analysis, Computer-Assisted Pattern recognition Pattern Recognition, Automated - methods propagation mechanisms Reproducibility of Results Sensitivity and Specificity Signal Processing, Computer-Assisted Studies Topology Trees triangular norms and co-norms two-stage design |
| title | Genetically optimized fuzzy decision trees |
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