Barros, Rodrigo C. <br/><br/>
Automatic Design of Decision-Tree Induction Algorithms / 
by Rodrigo C. Barros, André C.P.L.F de Carvalho, Alex A. Freitas. 
 - 1 online resource 
 - SpringerBriefs in Computer Science,  2191-5768 .
<br/><br/>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. 
<br/><br/> 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. 
<br/><br/><label>ISBN: </label>9783319142319 
<br/><br/><label>Standard No.: </label>10.1007/978-3-319-14231-9  doi 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Data mining.<br/>Optical pattern recognition.<br/>Data Mining and Knowledge Discovery.<br/>Pattern Recognition.
<br/><br/><label>LC Class. No.: </label>QA76.9.D343