Keywords
Affiliated Institutions
Related Publications
Chapter 18 Committees of decision trees
Many intelligent systems are designed to sift through a mass of evidence and arrive at a decision. Certain pieces of evidence may be given more weight than others, and this may ...
Best-first Decision Tree Learning
Decision trees are potentially powerful predictors and explicitly represent the structure of a dataset. Standard decision tree learners such as C4.5 expand nodes in depth-first ...
A Communication-Efficient Parallel Algorithm for Decision Tree
Decision tree (and its extensions such as Gradient Boosting Decision Trees and Random Forest) is a widely used machine learning algorithm, due to its practical effectiveness and...
Bagging, boosting, and C4.S
Breiman's bagging and Freund and Schapire's boosting are recent methods for improving the predictive power of classifier learning systems. Both form a set of classifiers that ar...
A further comparison of splitting rules for decision-tree induction
One approach to learning classification rules from examples is to build decision trees. A review and comparison paper by Mingers (Mingers, 1989) looked at the first stage of tre...
Publication Info
- Year
- 1995
- Type
- book-chapter
- Pages
- 21-29
- Citations
- 102
- Access
- Closed
External Links
Social Impact
Social media, news, blog, policy document mentions
Citation Metrics
Cite This
Identifiers
- DOI
- 10.1016/b978-1-55860-377-6.50012-8