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001 978-3-319-00960-5
003 DE-He213
005 20200420220216.0
007 cr nn 008mamaa
008 130911s2014 gw | s |||| 0|eng d
020 _a9783319009605
_9978-3-319-00960-5
024 7 _a10.1007/978-3-319-00960-5
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aGrąbczewski, Krzysztof.
_eauthor.
245 1 0 _aMeta-Learning in Decision Tree Induction
_h[electronic resource] /
_cby Krzysztof Grąbczewski.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXVI, 343 p. 33 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v498
505 0 _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.
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.  .
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aArtificial Intelligence (incl. Robotics).
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319009599
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v498
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-00960-5
912 _aZDB-2-ENG
942 _cEBK
999 _c51612
_d51612