Genetic Approach to Constructive Induction Based on Non-algebraic Feature Representation
The aim of constructive induction (CI) is to transform the original data representation of hard concepts with complex interaction into one that outlines the relation among attributes. CI methods based on greedy search suffer from the local optima problem because of high variation in the search space...
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| description | The aim of constructive induction (CI) is to transform the original data representation of hard concepts with complex interaction into one that outlines the relation among attributes. CI methods based on greedy search suffer from the local optima problem because of high variation in the search space of hard learning problems. To reduce the local optima problem, we propose a CI method based on genetic (evolutionary) algorithms. The method comprises two integrated genetic algorithms to construct functions over subsets of attributes in order to highlight regularities for the learner. Using non-algebraic representation for constructed functions assigns an equal degree of complexity to functions. This reduces the difficulty of constructing complex features. Experiments show that our method is comparable with and in some cases superior to existing CI methods. |
| doi_str_mv | 10.1007/978-3-540-45231-7_55 |
| format | Book Chapter |
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Experiments show that our method is comparable with and in some cases superior to existing CI methods.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Genetic Algorithm</subject><subject>Genetic Operator</subject><subject>Learning and adaptive systems</subject><subject>Local Optimum</subject><subject>Relevant Attribute</subject><subject>Search Space</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540408130</isbn><isbn>3540408134</isbn><isbn>9783540452317</isbn><isbn>3540452311</isbn><fulltext>true</fulltext><rsrctype>book_chapter</rsrctype><creationdate>2003</creationdate><recordtype>book_chapter</recordtype><recordid>eNpFUE1LLDEQjJ-8RfcfeJiLxzyT9GSSHHXxC-Q9EAVvoSeb0dV1Zkyygv_eHhVsGrqprmqoYuxIir9SCHPijOXAdS14rRVIbrzWW2xOMBD4hZltNpONlBygdju_N2EliF02EyAUd6aGfTZzRCHY1X_YPOdnQQVKOGtn7OEy9rGsQnU6jmnA8FSVoVoMfS5pE8rqPVbX_XLahr46wxyXFS3_hp7j-jG2CUl5EbFsUqxu45hijn3BiX3I9jpc5zj_mQfs_uL8bnHFb_5fXi9Ob_gI0hbedcp2qPUyCMROR6Nc60KN1rRKdKpttJBCEwuaZWtCsCq0UIOSypJTsnfAjr__jpgDrruEfVhlP6bVK6YPL7VuDGhNPPXNy3TqH2Py7TC8ZC-FnyL3lJ8HTwn6r3j9FDmJ4Od5Gt42MRcfJ1UgkwnX4QnHElP2IKw11vlGUiv4BLUjfuo</recordid><startdate>2003</startdate><enddate>2003</enddate><creator>Shafti, Leila S.</creator><creator>Pérez, Eduardo</creator><general>Springer Berlin / Heidelberg</general><general>Springer Berlin Heidelberg</general><general>Springer</general><scope>FFUUA</scope><scope>IQODW</scope></search><sort><creationdate>2003</creationdate><title>Genetic Approach to Constructive Induction Based on Non-algebraic Feature Representation</title><author>Shafti, Leila S. ; Pérez, Eduardo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-p318t-ff28fa55dc0aaf5e729b9c4a87b20f2b650105ff236db7cc82cb3432128081743</frbrgroupid><rsrctype>book_chapters</rsrctype><prefilter>book_chapters</prefilter><language>eng</language><creationdate>2003</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Genetic Algorithm</topic><topic>Genetic Operator</topic><topic>Learning and adaptive systems</topic><topic>Local Optimum</topic><topic>Relevant Attribute</topic><topic>Search Space</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shafti, Leila S.</creatorcontrib><creatorcontrib>Pérez, Eduardo</creatorcontrib><collection>ProQuest Ebook Central - Book Chapters - Demo use only</collection><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shafti, Leila S.</au><au>Pérez, Eduardo</au><au>Berthold, Michael R</au><au>Borgelt, Christian</au><au>Kruse, Rudolf</au><au>Bradley, Elizabeth</au><au>Lenz, Hans-Joachim</au><format>book</format><genre>bookitem</genre><ristype>CHAP</ristype><atitle>Genetic Approach to Constructive Induction Based on Non-algebraic Feature Representation</atitle><btitle>Advances in Intelligent Data Analysis V</btitle><seriestitle>Lecture Notes in Computer Science</seriestitle><date>2003</date><risdate>2003</risdate><volume>2810</volume><spage>599</spage><epage>610</epage><pages>599-610</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540408130</isbn><isbn>3540408134</isbn><eisbn>9783540452317</eisbn><eisbn>3540452311</eisbn><abstract>The aim of constructive induction (CI) is to transform the original data representation of hard concepts with complex interaction into one that outlines the relation among attributes. 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| ispartof | Advances in Intelligent Data Analysis V, 2003, Vol.2810, p.599-610 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_15567355 |
| source | Springer Books |
| subjects | Applied sciences Artificial intelligence Computer science control theory systems Exact sciences and technology Genetic Algorithm Genetic Operator Learning and adaptive systems Local Optimum Relevant Attribute Search Space |
| title | Genetic Approach to Constructive Induction Based on Non-algebraic Feature Representation |
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