Weighted Bagging in Decision Trees: Data Mining

Bagging Soft Decision Trees

The decision tree is one of the earliest predictive models in machine learning. In the soft decision tree, based on the hierarchical mixture of experts model, internal binary nodes take soft decisions and choose both children with probabilities given by a sigmoid gating function. Hence for an input, all the paths to all the leaves are traversed and all those leaves contribute to the final decis...

Bagging Decision Multi-trees

Ensemble methods improve accuracy by combining the predictions of a set of different hypotheses. A well-known method for generating hypothesis ensembles is Bagging. One of the main drawbacks of ensemble methods in general, and Bagging in particular, is the huge amount of computational resources required to learn, store, and apply the set of models. Another problem is that even using the bootstr...

„data Mining” Decision Trees Algorithms Optimization

Weighted Decision Trees

While decision tree compilation is a promising way to carry out guard tests eeciently, the methods given in the literature do not take into account either the execution characteristics of the program or the machine-level tradeoos between diierent ways to implement branches. These methods therefore ooer little or no guidance for the implementor with regard to how decision trees are to be realize...

Data mining with decision trees and decision rules

This paper describes the use of decision tree and rule induction in data mining applications. Of methods for classi cation and regression that have been developed in the elds of pattern recognition, statistics, and machine learning, these are of particular interest for data mining since they utilize symbolic and interpretable representations. Symbolic solutions can provide a high degree of insi...