C4.5: programs for machine learning
C4.5: programs for machine learning
Decision Trees: An Overview and Their Use in Medicine
Journal of Medical Systems
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
An experimental comparative study of web mining methods for recommender systems
DIWED'06 Proceedings of the 6th WSEAS International Conference on Distance Learning and Web Engineering
Improving Performance of a Binary Classifier by Training Set Selection
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Implementation of classifiers for choosing insurance policy using decision trees: a case study
WSEAS Transactions on Computers
Fuzzy Expert System Design for Diagnosis of Liver Disorders
KAM '08 Proceedings of the 2008 International Symposium on Knowledge Acquisition and Modeling
An intelligent model for liver disease diagnosis
Artificial Intelligence in Medicine
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Classification of wetland from TM imageries based on decision tree
WSEAS Transactions on Information Science and Applications
Improved mining of software complexity data on evolutionary filtered training sets
WSEAS Transactions on Information Science and Applications
Data Mining with Decision Trees: Theroy and Applications
Data Mining with Decision Trees: Theroy and Applications
A hierarchical shrinking decision tree for imbalanced datasets
DNCOCO'06 Proceedings of the 5th WSEAS international conference on Data networks, communications and computers
Hi-index | 0.00 |
The complexity of liver makes it easily affected by disease of disorder. So diagnosing liver disorder disease is a high interest to data miners, and decision trees have been useful data mining tools to diagnose the disease, but the accuracy of decision trees has been limited due to insufficient data. In order to generate more accurate decision trees for liver disorder disease this paper suggests a method based on over-sampling in minor classes to compensate the insufficiency of data effectively. Experiments were done with two representative algorithms of decision trees, C4.5 and CART, and a data set, 'BUPA liver disorder', and showed the validity of the method.