Automatic Design of Decision-Tree Induction Algorithms

Sold by Ingram

This product may not be approved for your region.
Paperback / softback
  • Free Shipping

    On orders of AED 100 or more. Standard delivery within 5-15 days.
  • Free Reserve & Collect

    Reserve & Collect from Magrudy's or partner stores accross the UAE.
  • Cash On Delivery

    Pay when your order arrives.
  • Free returns

    See more about our return policy.
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.