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03406nam a22005175i 4500 |
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978-3-319-14231-9 |
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DE-He213 |
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20210618050318.0 |
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cr nn 008mamaa |
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150204s2015 gw | s |||| 0|eng d |
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|a 9783319142319
|9 978-3-319-14231-9
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|a 10.1007/978-3-319-14231-9
|2 doi
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|a QA76.9.D343
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|a UNF
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|a 006.312
|2 23
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|a Barros, Rodrigo C.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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|a Automatic Design of Decision-Tree Induction Algorithms
|h [electronic resource] /
|c by Rodrigo C. Barros, André C.P.L.F de Carvalho, Alex A. Freitas.
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| 250 |
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|a 1st ed. 2015.
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| 264 |
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2015.
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| 300 |
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|a XII, 176 p. 18 illus.
|b online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 347 |
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|a text file
|b PDF
|2 rda
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| 490 |
1 |
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|a SpringerBriefs in Computer Science,
|x 2191-5768
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| 505 |
0 |
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|a Introduction -- Decision-Tree Induction -- Evolutionary Algorithms and Hyper-Heuristics -- HEAD-DT: Automatic Design of Decision-Tree Algorithms -- HEAD-DT: Experimental Analysis -- HEAD-DT: Fitness Function Analysis -- Conclusions.
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|a Presents a detailed study of the major design components that constitute a top-down decision-tree induction algorithm, including aspects such as split criteria, stopping criteria, pruning and the approaches for dealing with missing values. Whereas the strategy still employed nowadays is to use a 'generic' decision-tree induction algorithm regardless of the data, the authors argue on the benefits that a bias-fitting strategy could bring to decision-tree induction, in which the ultimate goal is the automatic generation of a decision-tree induction algorithm tailored to the application domain of interest. For such, they discuss how one can effectively discover the most suitable set of components of decision-tree induction algorithms to deal with a wide variety of applications through the paradigm of evolutionary computation, following the emergence of a novel field called hyper-heuristics. "Automatic Design of Decision-Tree Induction Algorithms" would be highly useful for machine learning and evolutionary computation students and researchers alike.
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|a Data mining.
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| 650 |
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|a Pattern recognition.
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| 650 |
1 |
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|a Data Mining and Knowledge Discovery.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I18030
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| 650 |
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|a Pattern Recognition.
|0 https://scigraph.springernature.com/ontologies/product-market-codes/I2203X
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| 700 |
1 |
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|a de Carvalho, André C.P.L.F.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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| 700 |
1 |
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|a Freitas, Alex A.
|e author.
|4 aut
|4 http://id.loc.gov/vocabulary/relators/aut
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| 710 |
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|a SpringerLink (Online service)
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| 773 |
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|t Springer Nature eBook
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| 776 |
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|i Printed edition:
|z 9783319142326
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| 776 |
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8 |
|i Printed edition:
|z 9783319142302
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| 830 |
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|a SpringerBriefs in Computer Science,
|x 2191-5768
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| 856 |
4 |
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|u https://doi.org/10.1007/978-3-319-14231-9
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| 912 |
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|a ZDB-2-SCS
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| 912 |
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|a ZDB-2-SXCS
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| 950 |
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|a Computer Science (SpringerNature-11645)
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| 950 |
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|a Computer Science (R0) (SpringerNature-43710)
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