Discovery of Significant Classification Rules from Incrementally Inducted Decision Tree Ensemble for Diagnosis of Disease

Authors:
Minghao Piao;Jong Bum Lee;Khalid E. Saeed;Keun Ho Ryu
Affiliations:
Database/Bioinformatics Lab, Chungbuk National University, Cheongju, Korea 361-763;Database/Bioinformatics Lab, Chungbuk National University, Cheongju, Korea 361-763;Database/Bioinformatics Lab, Chungbuk National University, Cheongju, Korea 361-763;Database/Bioinformatics Lab, Chungbuk National University, Cheongju, Korea 361-763
Venue:
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Year:
2009

Citing 8
Cited 0

C4.5: programs for machine learning

C4.5: programs for machine learning
Bagging predictors

Machine Learning
Decision Tree Induction Based on Efficient Tree Restructuring

Machine Learning
Incremental Induction of Decision Trees

Machine Learning
Induction of Decision Trees

Machine Learning
Decision Tree Induction Based on Efficient Tree Restructuring

Decision Tree Induction Based on Efficient Tree Restructuring
Ensembles of Cascading Trees

ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining biosignal data: coronary artery disease diagnosis using linear and nonlinear features of HRV

PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

Previous studies show that using significant classification rules to accomplish the classification task is suitable for bio-medical research. Discovery of many significant rules could be performed by using ensemble methods in decision tree induction. However, those traditional approaches are not useful for incremental task. In this paper, we use an ensemble method named Cascading and Sharing to derive many significant classification rules from incrementally inducted decision tree and improve the classifiers accuracy.