@article{725525,
      recid = {725525},
      author = {Barros, Rodrigo C., and Carvalho, André C.P.L.F. de, and  Freitas, Alex A.,},
      title = {Automatic Design of Decision-Tree Induction Algorithms  [electronic resource] /},
      pages = {1 online resource.},
      abstract = {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.},
      url = {http://library.usi.edu/record/725525},
      doi = {https://doi.org/10.1007/978-3-319-14231-9},
}