VPRSM Based Decision Tree Classifier
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Summary
The criterion for node selection in the new method is based on the measurement of the variable precision explicit regions corresponding to candidate attributes, and the presented approach is compared with C4.5 on some data sets from the UCI machine learning repository, which instantiates the feasibility of the proposed method.
- Type
- article
- Published
- 2007-01-01
- Cited by
- 8
- References
- 16
- OpenAlex
- https://openalex.org/W77954177
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:35559894
Keywords
Rough set, Decision tree, Computer science, Data mining, Entropy (arrow of time)
References
- An Empirical Comparison of Pruning Methods for Decision Tree Induction
- Multisurface method of pattern separation for medical diagnosis applied to breast cytology.
- Data mining and rough set theory
- Rough Sets: Probabilistic versus Deterministic Approach
- Inferring Decision Trees Using the Minimum Description Length Principle
- Variable Precision Rough Set Model
- Rough set approach to multi-attribute decision analysis
- Programs for Machine Learning. Part I
- Probabilistic Decision Tables in the Variable Precision Rough Set Model
- MACHINE LEARNING An Artificial Intelligence Approach
- Maintenance of Reducts in the Variable Precision Rough Set Model
- ROUGH SET BASED APPROACH TO SELECTION OF NODE
- Imprecise Concept Learning within a Growing Language
- An Introduction to Decision Trees
- Rough Sets
Cited by
- A Survey of Various Tree Based Classification Techniques
- An optimal tree based classification technique using enhanced variable precision explicit region
- Boosted Test-FDA: a transductive boosting method
- Parameter Selection and Uncertainty Measurement for Variable Precision Probabilistic Rough Set
- Application of Fuzzy Decision Trees in Analog Forecasting
- Motor Imagery Classification Based on Variable Precision Multigranulation Rough Set and Game Theoretic Rough Set
- in Computer
- Swarm Intelligence and Variable Precision Rough Set Model: A Hybrid Approach for Classification
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