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Automatic design of decision-tree Induction algorithms [electronic resource]
- 作者： Barros, Rodrigo C.
- 其他作者：
- 其他題名：
- SpringerBriefs in computer science,
- 出版： Cham : Springer International Publishing :Imprint: Springer
- 叢書名： SpringerBriefs in computer science,
- 主題： Computer algorithms , Decision Trees , Computer science , Data Mining and Knowledge Discovery. , Pattern Recognition
- ISBN： 9783319142319 (electronic bk.) 、 9783319142302 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
- 內容註: 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.
- 摘要註: 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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讀者標籤：
- 系統號： 005128003 | 機讀編目格式
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