Download Automatic Design of Decision-Tree Induction Algorithms by Rodrigo C. Barros, André C.P.L.F de Carvalho, Alex A. PDF

By Rodrigo C. Barros, André C.P.L.F de Carvalho, Alex A. Freitas

Presents a close learn of the foremost layout parts that represent a top-down decision-tree induction set of rules, together with facets resembling break up standards, preventing standards, pruning and the ways for facing lacking values. while the tactic nonetheless hired these days is to take advantage of a 'generic' decision-tree induction set of rules whatever the info, the authors argue at the advantages bias-fitting approach might deliver to decision-tree induction, within which the final word objective is the automated new release of a decision-tree induction set of rules adapted to the appliance area of curiosity. For such, they talk about how you can successfully detect the main compatible set of parts of decision-tree induction algorithms to accommodate a large choice of functions throughout the paradigm of evolutionary computation, following the emergence of a unique box known as hyper-heuristics.

"Automatic layout of Decision-Tree Induction Algorithms" will be hugely invaluable for computer studying and evolutionary computation scholars and researchers alike.

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LMDT uses heuristics to determine when a linear machine has stabilized (since convergence cannot be guaranteed). More specifically, for handling non-linearly separable problems, a method similar to simulated annealing (SA) is used (called thermal training). Draper and Brodley [30] show how LMDT can be altered to induce decision trees that minimize arbitrary misclassification cost functions. SADT (Simulated Annealing of Decision Trees) [47] is a system that employs SA for finding good coefficient values for attributes in non-terminal nodes of decision trees.

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