{ "item_title" : "Automatic Design of Decision-Tree Induction Algorithms", "item_author" : [" Rodrigo C. Barros", "André C. P. L. F. de Carvalho", "Alex a. Freitas "], "item_description" : "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.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/3/31/914/230/3319142305_b.jpg", "price_data" : { "retail_price" : "59.99", "online_price" : "59.99", "our_price" : "59.99", "club_price" : "59.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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