C4.5: programs for machine learning
C4.5: programs for machine learning
Variable precision rough set model
Journal of Computer and System Sciences
A New Criterion in Selection and Discretization of Attributes for the Generation of Decision Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the well-behavedness of important attribute evaluation functions
SCAI '97 Proceedings of the sixth Scandinavian conference on Artificial intelligence
General and Efficient Multisplitting of Numerical Attributes
Machine Learning
RainForest—A Framework for Fast Decision Tree Construction of Large Datasets
Data Mining and Knowledge Discovery
Machine Learning
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
An Improved Attribute Selection Measure for Decision Tree Induction
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04
Rough set based approach for inducing decision trees
Knowledge-Based Systems
An Algorithm for Constructing Decision Tree Based on Variable Precision Rough Set Model
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 01
Constructing a decision tree from data with hierarchical class labels
Expert Systems with Applications: An International Journal
MMDT: a multi-valued and multi-labeled decision tree classifier for data mining
Expert Systems with Applications: An International Journal
ComEnVprs: a novel approach for inducing decision tree classifiers
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Classifiability-based omnivariate decision trees
IEEE Transactions on Neural Networks
Special issue: Hybrid approaches for approximate reasoning
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Hybrid approaches for approximate reasoning
Linear programming with rough interval coefficients
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hi-index | 0.00 |
This paper analyzes the existing decision tree classification algorithms and finds that these algorithms based on variable precision rough set VPRS have better classification accuracy and can tolerate the noise data. But when constructing decision tree based on variable precision rough set, these algorithms have the following shortcomings: the choice of attribute is difficult and the decision tree classification accuracy is not high. Therefore, this paper proposes a new variable precision rough set based decision tree algorithm IVPRSDT. This algorithm uses a new standard of attribute selection which considers comprehensively the classification accuracy and number of attribute values, that is, weighted roughness and complexity. At the same time support and confidence are introduced in the conditions of the corresponding node to stop splitting, and they can improve the algorithm's generalization ability. To reduce the impact of noise data and missing values, IVPRSDT uses the label predicted method based on match. The comparing experiments on twelve different data sets from the UCI Machine Learning Repository show that IVPRSDT can effectively improve the classification accuracy.