| Description |
XII, 176 p. 18 illus. online resource. |
| Content |
text |
| Carrier |
online resource |
| Description |
text file PDF rda |
| Series |
SpringerBriefs in Computer Science, 2191-5768 |
|
Springer computer science 2015-2017.
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SpringerBriefs in Computer Science, 2191-5768
|
| Contents |
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. |
| Summary |
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. |
| Subject |
Data mining.
|
|
Optical pattern recognition.
|
| Added Author |
de Carvalho, André C.P.L.F.
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Freitas, Alex A.
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|
SpringerLink (Online service)
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| Other Form: |
Printed edition: 9783319142326 |
|
Printed edition: 9783319142302 |
| ISBN |
9783319142319 |
| Standard No. |
10.1007/978-3-319-14231-9 doi |
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