A Further Comparison of Splitting Rules for Decision-Tree Induction
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Summary
The results indicate that random splitting leads to increased error and are at variance with those presented by Mingers.
- Type
- article
- Published
- 1992-01-03
- Cited by
- 81
- References
- 13
- Access
- Open access
- OpenAlex
- https://openalex.org/W1969223365
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:41596205
Keywords
Variance (accounting), Decision tree, Random forest, Mathematics, Algorithm
References
- ASSISTANT 86: A Knowledge-Elicitation Tool for Sophisticated Users
- An Empirical Comparison of Pattern Recognition, Neural Nets, and Machine Learning Classification Methods
- Inductive knowledge acquisition: a case study
- An Empirical Comparison of Selection Measures for Decision-Tree Induction
- THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS
- An Experimental Comparison of Symbolic and Connectionist Learning Algorithms
- The CN2 Induction Algorithm
- Machine learning as an experimental science
- Induction of decision trees
- Simplifying decision trees
- Incremental learning from noisy data
- Unknown Attribute Values in Induction
- Learning Classification Rules Using Bayes
Cited by
- Reconstruction of an Expert's Decision Making Expertise in Concrete Dispatching by Machine Learning
- Classification of Students using their Data Traffic within an e-Learning Platform
- Decision Tree Induction: Data Classification using Height-Balanced Tree
- Intelligent systems for knowledge modeling and discovery
- From Decision Trees to Classification Rules with Data Representing User Traffic from an e-Learning Platform
- An Exact Probability Metric for Decision Tree Splitting and Stopping
- Inducing Interpretable Voting Classifiers without Trading Accuracy for Simplicity: Theoretical Results, Approximation Algorithms, and Experiments
- Separate-and-Conquer Rule Learning
- Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
- Metric Regression Forests for Correspondence Estimation
- ID+: Enhancing Medical Knowledge Acquisition with Machine Learning
- CSNL: A cost-sensitive non-linear decision tree algorithm
- A test sheet generating algorithm based on intelligent genetic algorithm and hierarchical planning
- Encouraging experimental results on learning CNF
- BEXA: A Covering Algorithm for Learning Propositional Concept Descriptions
- Vague knowledge search in the design for outsourcing using fuzzy decision tree
- Research on new data mining method based on hybrid genetic algorithm
- Technical Note: Some Properties of Splitting Criteria
- Extremely randomized trees
- A self explanatory review of decision tree classifiers
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