A philosophical basis for knowledge acquisition
Knowledge Acquisition
Original Contribution: Stacked generalization
Neural Networks
Induction of ripple-down rules applied to modeling large databases
Journal of Intelligent Information Systems
Verification and validation with ripple-down rules
International Journal of Human-Computer Studies - Special issue: verification and validation
Machine Learning
Taking up the situated cognition challenge with ripple down rules
International Journal of Human-Computer Studies
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
A description length-based decision criterion for default knowledge in the ripple down rules method
Knowledge and Information Systems
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Stacking Bagged and Dagged Models
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Knowledge in Context: A Strategy for Expert System Maintenance
AI '88 Proceedings of the 2nd Australian Joint Artificial Intelligence Conference
An Evaluation of Grading Classifiers
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Automated classification reveals morphological factors associated with dementia
Applied Soft Computing
Adaptive Ripple Down Rules method based on minimum description length principle
Intelligent Data Analysis
The lack of a priori distinctions between learning algorithms
Neural Computation
A social software/Web 2.0 approach to collaborative knowledge engineering
Information Sciences: an International Journal
An Incremental Knowledge Acquisition Method for Improving Duplicate Invoices Detection
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Two decades of ripple down rules research
The Knowledge Engineering Review
Generalising Symbolic Knowledge in Online Classification and Prediction
Knowledge Acquisition: Approaches, Algorithms and Applications
Discovering Areas of Expertise from Publication Data
Knowledge Acquisition: Approaches, Algorithms and Applications
Incremental knowledge acquisition using generalised RDR for soccer simulation
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
RDRCE: combining machine learning and knowledge acquisition
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Simulated assessment of ripple round rules
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
The Ballarat incremental knowledge engine
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Online knowledge validation with prudence analysis in a document management application
Expert Systems with Applications: An International Journal
A new model for classifying DNA code inspired by neural networks and FSA
PKAW'06 Proceedings of the 9th Pacific Rim Knowledge Acquisition international conference on Advances in Knowledge Acquisition and Management
Clustering algorithms for ITS sequence data with alignment metrics
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Applying multiple classification ripple round rules to a complex configuration task
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Applying clustering and ensemble clustering approaches to phishing profiling
AusDM '09 Proceedings of the Eighth Australasian Data Mining Conference - Volume 101
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
Predicting cardiac autonomic neuropathy category for diabetic data with missing values
Computers in Biology and Medicine
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It is well known that classification models produced by the Ripple Down Rules are easier to maintain and update. They are compact and can provide an explanation of their reasoning making them easy to understand for medical practitioners. This article is devoted to an empirical investigation and comparison of several ensemble methods based on Ripple Down Rules in a novel application for the detection of cardiovascular autonomic neuropathy (CAN) from an extensive data set collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University. Our experiments included essential ensemble methods, several more recent state-of-the-art techniques, and a novel consensus function based on graph partitioning. The results show that our novel application of Ripple Down Rules in ensemble classifiers for the detection of CAN achieved better performance parameters compared with the outcomes obtained previously in the literature.