| 000 | 03029nam a22005055i 4500 | ||
|---|---|---|---|
| 001 | 978-3-319-14231-9 | ||
| 003 | DE-He213 | ||
| 005 | 20200421112545.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 150204s2015 gw | s |||| 0|eng d | ||
| 020 |
_a9783319142319 _9978-3-319-14231-9 |
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| 024 | 7 |
_a10.1007/978-3-319-14231-9 _2doi |
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| 050 | 4 | _aQA76.9.D343 | |
| 072 | 7 |
_aUNF _2bicssc |
|
| 072 | 7 |
_aUYQE _2bicssc |
|
| 072 | 7 |
_aCOM021030 _2bisacsh |
|
| 082 | 0 | 4 |
_a006.312 _223 |
| 100 | 1 |
_aBarros, Rodrigo C. _eauthor. |
|
| 245 | 1 | 0 |
_aAutomatic Design of Decision-Tree Induction Algorithms _h[electronic resource] / _cby Rodrigo C. Barros, Andr�e C.P.L.F de Carvalho, Alex A. Freitas. |
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2015. |
|
| 300 |
_aXII, 176 p. 18 illus. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 490 | 1 |
_aSpringerBriefs in Computer Science, _x2191-5768 |
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| 505 | 0 | _aIntroduction -- 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. | |
| 520 | _aPresents 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. | ||
| 650 | 0 | _aComputer science. | |
| 650 | 0 | _aData mining. | |
| 650 | 0 | _aPattern recognition. | |
| 650 | 1 | 4 | _aComputer Science. |
| 650 | 2 | 4 | _aData Mining and Knowledge Discovery. |
| 650 | 2 | 4 | _aPattern Recognition. |
| 700 | 1 |
_ade Carvalho, Andr�e C.P.L.F. _eauthor. |
|
| 700 | 1 |
_aFreitas, Alex A. _eauthor. |
|
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer eBooks | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783319142302 |
| 830 | 0 |
_aSpringerBriefs in Computer Science, _x2191-5768 |
|
| 856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-319-14231-9 |
| 912 | _aZDB-2-SCS | ||
| 942 | _cEBK | ||
| 999 |
_c58492 _d58492 |
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