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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.

Provides a detailed and up-to-date view on the top-down induction of decision treesIntroduces a novel hyper-heuristic approach that is capable of automatically designing top-down decision-tree induction algorithmsDiscusses two frameworks in which the hyper-heuristic can be executed in order to generate tailor-made decision-tree induction algorithmsIncludes supplementary material: sn.pub/extras