Download e-book for iPad: Automatic Design of Decision-Tree Induction Algorithms by Rodrigo C. Barros, André C. P. L. F. de Carvalho, Alex A.

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

ISBN-10: 3319142305

ISBN-13: 9783319142302

Provides an in depth research of the main layout elements that represent a top-down decision-tree induction set of rules, together with facets similar to cut 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 facts, the authors argue at the advantages bias-fitting process may deliver to decision-tree induction, within which the final word aim is the automated new release of a decision-tree induction set of rules adapted to the applying area of curiosity. For such, they talk about how you can successfully observe the main appropriate set of parts of decision-tree induction algorithms to accommodate a wide selection 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 helpful for computing device studying and evolutionary computation scholars and researchers alike.

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Additional resources for Automatic Design of Decision-Tree Induction Algorithms (Springer Briefs in Computer Science)

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This problem is easy to solve optimally: simply sort all the values U j , and consider setting wi to the midpoint between each pair of different class values. For each distinct placement of the coefficient wi , OC1 computes the impurity of the resulting split, and replaces original coefficient wi by the recently discovered value if there is reduction on impurity. The pseudocode of this deterministic perturbation method is presented in Algorithm 2. The parameter Pstag (stagnation probability) is the probability that a hyperplane is perturbed to a location that does not change the impurity measure.

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.

5 [89], and we ended the discussion on top-down induction with an enumeration of possible strategies for dealing with missing values, either in the growing phase or during classification of a new instance. We ended our analysis on decision trees with some alternative induction strategies, such as bottom-up induction and hybrid-induction. In addition, we briefly discussed work that attempt to avoid the greedy strategy, by either implementing lookahead techniques, evolutionary algorithms, beam-search, linear programming, (non-) incremental restructuring, skewing, or anytime learning.

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Automatic Design of Decision-Tree Induction Algorithms (Springer Briefs in Computer Science) by Rodrigo C. Barros, André C. P. L. F. de Carvalho, Alex A. Freitas

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