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What is Decision Tree Pruning

Encyclopedia of Information Science and Technology, Fourth Edition
An approach that reduces the size of decision tree by removing parts of the tree that provide little power to classify/predict the data. This technique improves the generalization power of the decision tree and reduces the over-fitting.
Published in Chapter:
Evolutionary Algorithms for Global Decision Tree Induction
Marek Kretowski (Bialystok University of Technology, Poland) and Marcin Czajkowski (Bialystok University of Technology, Poland)
Copyright: © 2018 |Pages: 10
DOI: 10.4018/978-1-5225-2255-3.ch185
Abstract
Decision trees represent one of the main predictive techniques in knowledge discovery. This chapter describes evolutionary induced trees, which are emerging alternatives to the greedy top-down solutions. Most typical tree-based system searches only for locally optimal decisions at each node and do not guarantee the optimal solution. Application of evolutionary algorithms to the problem of decision tree induction allows searching for the structure of the tree, tests in internal nodes and regression functions in the leaves (for model trees) at the same time. As a result, such globally induced decision tree is able to avoid local optima and usually leads to better prediction than the greedy counterparts.
Full Text Chapter Download: US $37.50 Add to Cart
More Results
Global Induction of Classification and Regression Trees
An approach that reduces the size of decision tree by removing parts of the tree that provide little power to classify/predict the data. This technique improves the generalization power of the decision tree and reduces the over-fitting.
Full Text Chapter Download: US $37.50 Add to Cart
Evolutionary Algorithms for Global Decision Tree Induction
An approach that reduces the size of decision tree by removing parts of the tree that provide little power to classify/predict the data. This technique improves the generalization power of the decision tree and reduces the over-fitting.
Full Text Chapter Download: US $37.50 Add to Cart
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