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000695484 0167_ $$a016524527$$2Uk
000695484 020__ $$a9783319009605 $$qelectronic book
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000695484 020__ $$z9783319009599
000695484 0247_ $$a10.1007/978-3-319-00960-5$$2doi
000695484 035__ $$aSP(OCoLC)ocn859253554
000695484 035__ $$aSP(OCoLC)859253554
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000695484 049__ $$aISEA
000695484 050_4 $$aQ342
000695484 08204 $$a006.3$$223
000695484 1001_ $$aGra̧bczewski, Krzysztof,$$eauthor.
000695484 24510 $$aMeta-learning in decision tree induction$$h[electronic resource]  /$$cKrzysztof Grąbczewski.
000695484 264_1 $$aCham :$$bSpringer,$$c[2013?]
000695484 264_4 $$c©2014
000695484 300__ $$a1 online resource (xvi, 343 pages) :$$billustrations.
000695484 336__ $$atext$$btxt$$2rdacontent
000695484 337__ $$acomputer$$bc$$2rdamedia
000695484 338__ $$aonline resource$$bcr$$2rdacarrier
000695484 4901_ $$aStudies in Computational Intelligence,$$x1860-949X ;$$v498
000695484 504__ $$aIncludes bibliographical references and index.
000695484 5050_ $$aIntroduction -- Techniques of decision tree induction -- Multivariate decision trees -- Unified view of decision tree induction algorithms -- Intemi advanced meta-learning framework -- Meta-level analysis of decision tree induction.
000695484 506__ $$aAccess limited to authorized users.
000695484 520__ $$aThe book focuses on different variants of decision tree induction but also describes the meta-learning approach in general which is applicable to other types of machine learning algorithms. The book discusses different variants of decision tree induction and represents a useful source of information to readers wishing to review some of the techniques used in decision tree learning, as well as different ensemble methods that involve decision trees. It is shown that the knowledge of different components used within decision tree learning needs to be systematized to enable the system to generate and evaluate different variants of machine learning algorithms with the aim of identifying the top-most performers or potentially the best one. A unified view of decision tree learning enables to emulate different decision tree algorithms simply by setting certain parameters. As meta-learning requires running many different processes with the aim of obtaining performance results, a detailed description of the experimental methodology and evaluation framework is provided. Meta-learning is discussed in great detail in the second half of the book. The exposition starts by presenting a comprehensive review of many meta-learning approaches explored in the past described in literature, including for instance approaches that provide a ranking of algorithms. The approach described can be related to other work that exploits planning whose aim is to construct data mining workflows. The book stimulates interchange of ideas between different, albeit related, approaches.
000695484 588__ $$aDescription based on online resource; title from PDF title page (SpringerLink, viewed September 16, 2013).
000695484 650_0 $$aComputational intelligence.
000695484 650_0 $$aDecision trees.
000695484 830_0 $$aStudies in computational intelligence ;$$vv.498.
000695484 85280 $$bebk$$hSpringerLink
000695484 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://dx.doi.org/10.1007/978-3-319-00960-5$$zOnline Access
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000695484 980__ $$aEBOOK
000695484 980__ $$aBIB
000695484 982__ $$aEbook
000695484 983__ $$aOnline
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