Pruning the Boosting Ensemble of Decision Trees

Quickly Boosting Decision Trees - Pruning Underachieving Features Early

Boosted decision trees are among the most popular learning techniques in use today. While exhibiting fast speeds at test time, relatively slow training renders them impractical for applications with real-time learning requirements. We propose a principled approach to overcome this drawback. We prove a bound on the error of a decision stump given its preliminary error on a subset of the training...

Boosting Lazy Decision Trees

This paper explores the problem of how to construct lazy decision tree ensembles. We present and empirically evaluate a relevancebased boosting-style algorithm that builds a lazy decision tree ensemble customized for each test instance. From the experimental results, we conclude that our boosting-style algorithm significantly improves the performance of the base learner. An empirical comparison...

Boosting Decision Trees

A new boosting algorithm of Freund and Schapire is used to improve the performance of decision trees which are constructed usin: the information ratio criterion of Quinlan’s C4.5 algorithm. This boosting algorithm iteratively constructs a series of decision tress, each decision tree being trained and pruned on examples that have been filtered by previously trained trees. Examples that have been...

Pruning Decision Trees and Lists

Machine learning algorithms are techniques that automatically build models describing 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 domain from which the data originates. Decision trees and lists are potentially powerful predictors and embody an explicit representation of the struc...

Pruning Decision Trees and Lists