Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Machine Learning
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Stacking Bagged and Dagged Models
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
An Evaluation of Grading Classifiers
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Multistrategy Ensemble Learning: Reducing Error by Combining Ensemble Learning Techniques
IEEE Transactions on Knowledge and Data Engineering
Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Automated classification reveals morphological factors associated with dementia
Applied Soft Computing
The lack of a priori distinctions between learning algorithms
Neural Computation
A two-step classification approach to unsupervised record linkage
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
Efficient discovery of risk patterns in medical data
Artificial Intelligence in Medicine
FaSS: Ensembles for Stable Learners
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Resource-aware ECG analysis on mobile devices
Proceedings of the 2011 ACM Symposium on Applied Computing
Combined Feature Selection and Cancer Prognosis Using Support Vector Machine Regression
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
RA-SAX: Resource-Aware Symbolic Aggregate Approximation for Mobile ECG Analysis
MDM '11 Proceedings of the 2011 IEEE 12th International Conference on Mobile Data Management - Volume 01
Ensemble selection for superparent-one-dependence estimators
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
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
Self-organizing maps for translating health care knowledge: a case study in diabetes management
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Detection of CAN by ensemble classifiers based on ripple down rules
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
A multi-tier phishing detection and filtering approach
Journal of Network and Computer Applications
NBIS '12 Proceedings of the 2012 15th International Conference on Network-Based Information Systems
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
Applying clustering and ensemble clustering approaches to phishing profiling
AusDM '09 Proceedings of the Eighth Australasian Data Mining Conference - Volume 101
Understanding risk factors in cardiac rehabilitation patients with random forests and decision trees
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Using decision tree for diagnosing heart disease patients
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Empirical study of bagging predictors on medical data
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Predicting cardiac autonomic neuropathy category for diabetic data with missing values
Computers in Biology and Medicine
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This paper is devoted to empirical investigation of novel multi-level ensemble meta classifiers for the detection and monitoring of progression of cardiac autonomic neuropathy, CAN, in diabetes patients. Our experiments relied on an extensive database and concentrated on ensembles of ensembles, or multi-level meta classifiers, for the classification of cardiac autonomic neuropathy progression. First, we carried out a thorough investigation comparing the performance of various base classifiers for several known sets of the most essential features in this database and determined that Random Forest significantly and consistently outperforms all other base classifiers in this new application. Second, we used feature selection and ranking implemented in Random Forest. It was able to identify a new set of features, which has turned out better than all other sets considered for this large and well-known database previously. Random Forest remained the very best classifier for the new set of features too. Third, we investigated meta classifiers and new multi-level meta classifiers based on Random Forest, which have improved its performance. The results obtained show that novel multi-level meta classifiers achieved further improvement and obtained new outcomes that are significantly better compared with the outcomes published in the literature previously for cardiac autonomic neuropathy.