One of the well known and efficient techniques is decision trees, due to easy understanding structural output. But they may not always be easy to understand due to very big structural output. To overcome this short coming pruning can be used as a key procedure .It removes overusing noisy, ...
pruning techniques even though additional flexibility and increased performance can be obtained from this method. This dissertation reports on a study of cost-sensitive methods for decision tree pruning. A decision tree pruning algorithm called KBP1.0, which includes four cost-sensitive methods, is ...
We make sure that we are making the best decision for your trees and property. For instance, if a tree does not have to be removed, we will suggest suitable tree trimming or pruning techniques. In short, if you need your trees to be attractive, healthy, and stronger, you should get ...
Machine learning algorithms are techniques that automatically build models describ- ing the structure at the heart of a set of data. Ideally, such models can be used to predict properties of future data points and people can use them to analyze the ...
Pruning techniques are particularly important for state-of-the-artDecision TreeandRule Learningalgorithms (see there for more details). The key idea of pruning is essentially the same asRegularizationin statistical learning, with the key difference that regularization incorporates a complexity penalty dire...
Pre-pruning and Post-pruning are two standard techniques for handling noise in decision tree learning. Pre-pruning deals with noise during learning, while post-pruning addresses this problem after an overfitting theory has been learned. We first review several adaptations of pre- and post-pruning ...
In the terms of other compression techniques, Zhang et al. [373] presented an evolutionary embedding learning (EEL) paradigm to learn a fast and accurate student network via massive knowledge distillation. Compared with its baseline, EEL facilitated the bridge of the performance gap between teacher...
This paper discusses a revised form of decision tree pruning that is sensitive to the relative costs of the misclassification of examples. A brief overview of existing decision tree pruning methods is given together with the rationale behind these techniques. Then, the two types of misclassification...
In: Proceedings of the fourteenth international conference on machine learning. Morgan Kaufmann Publishers Inc, pp 211–218 Martinez-Muoz G, Hernandez-Lobato D, Suarez A (2009) An analysis of ensemble pruning techniques based on ordered aggregation. IEEE Trans Pattern Anal Mach Intell 31(2):245...
The application of fuzzy decision tree analysis in an exposition of the antecedents of audit fees Since the seminal work of Zadeh (Information Control 8 (1965) 338) fuzzy set theory (FST) has evolved into a valuable extension to traditional techniques, ... MJ Beynon,MJ Peel,YC Tang - 《...