C4.5: programs for machine learning
C4.5: programs for machine learning
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Machine Learning
Classification of meteorological volumetric radar data using rough set methods
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Characteristic relations for incomplete data: a generalization of the indiscernibility relation
Transactions on Rough Sets IV
An Integration of Cloud Transform and Rough Set Theory to Induction of Decision Trees
Fundamenta Informaticae - Fundamentals of Knowledge Technology
Mining incomplete data: a rough set approach
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
An Integration of Cloud Transform and Rough Set Theory to Induction of Decision Trees
Fundamenta Informaticae - Fundamentals of Knowledge Technology
Hi-index | 0.00 |
Many present methods for dealing with the continuous data and missing values in information systems for constructing decision tree do not perform well in practical applications. In this paper, a new algorithm, Decision Tree Construction based on the Cloud Transform and Rough Set Theory under Characteristic Relation (DTCCRSCR), is proposed for mining classification knowledge from the data set. The cloud transform is applied to discretize continuous data and the attribute whose weighted mean roughness under the characteristic relation is the smallest will be selected as the current splitting node. Experimental results show the decision trees constructed by DTCCRSCR tend to have a simpler structure, much higher classification accuracy and more understandable rules than C5.0 in most cases.