Machine Learning
Comparison of Feature Selection Strategies for Hearing Impairments Diagnostics
CBMS '02 Proceedings of the 15th IEEE Symposium on Computer-Based Medical Systems (CBMS'02)
An introduction to variable and feature selection
The Journal of Machine Learning Research
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
ICPPW '05 Proceedings of the 2005 International Conference on Parallel Processing Workshops
GMDH-based feature ranking and selection for improved classification of medical data
Journal of Biomedical Informatics
Cost-conscious classifier ensembles
Pattern Recognition Letters
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Correlation-based Feature Selection Strategy in Neural Classification
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
Ensemble Classifiers for Medical Diagnosis of Knee Osteoarthritis Using Gait Data
ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Comparison of classification accuracy using Cohen's Weighted Kappa
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Cancer classification using Rotation Forest
Computers in Biology and Medicine
CBMS '08 Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems
Relevance and Redundancy Analysis for Ensemble Classifiers
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
An experimental study on rotation forest ensembles
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Dimensionality reduction of protein mass spectrometry data using random projection
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Expert Systems with Applications: An International Journal
Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System
Journal of Medical Systems
A new hybrid intelligent system for accurate detection of Parkinson's disease
Computer Methods and Programs in Biomedicine
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Improving accuracies of machine learning algorithms is vital in designing high performance computer-aided diagnosis (CADx) systems. Researches have shown that a base classifier performance might be enhanced by ensemble classification strategies. In this study, we construct rotation forest (RF) ensemble classifiers of 30 machine learning algorithms to evaluate their classification performances using Parkinson's, diabetes and heart diseases from literature. While making experiments, first the feature dimension of three datasets is reduced using correlation based feature selection (CFS) algorithm. Second, classification performances of 30 machine learning algorithms are calculated for three datasets. Third, 30 classifier ensembles are constructed based on RF algorithm to assess performances of respective classifiers with the same disease data. All the experiments are carried out with leave-one-out validation strategy and the performances of the 60 algorithms are evaluated using three metrics; classification accuracy (ACC), kappa error (KE) and area under the receiver operating characteristic (ROC) curve (AUC). Base classifiers succeeded 72.15%, 77.52% and 84.43% average accuracies for diabetes, heart and Parkinson's datasets, respectively. As for RF classifier ensembles, they produced average accuracies of 74.47%, 80.49% and 87.13% for respective diseases. RF, a newly proposed classifier ensemble algorithm, might be used to improve accuracy of miscellaneous machine learning algorithms to design advanced CADx systems.