Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Machine Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A novel ensemble machine learning for robust microarray data classification
Computers in Biology and Medicine
An experimental study on rotation forest ensembles
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Robust feature selection by mutual information distributions
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Input Decimated Ensemble based on Neighborhood Preserving Embedding for spectrogram classification
Expert Systems with Applications: An International Journal
Microarray data classification based on ensemble independent component selection
Computers in Biology and Medicine
Reduced Reward-punishment editing for building ensembles of classifiers
Expert Systems with Applications: An International Journal
Greedy optimization classifiers ensemble based on diversity
Pattern Recognition
Rotation forest on microarray domain: PCA versus ICA
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction
Expert Systems with Applications: An International Journal
A GA based approach to improving the ICA based classification models for tumor classification
WSEAS Transactions on Information Science and Applications
Computer Methods and Programs in Biomedicine
WSEAS Transactions on Information Science and Applications
Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System
Journal of Medical Systems
Effective Diagnosis of Coronary Artery Disease Using The Rotation Forest Ensemble Method
Journal of Medical Systems
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We address the microarray dataset based cancer classification using a newly proposed multiple classifier system (MCS), referred to as Rotation Forest. To the best of our knowledge, it is the first time that Rotation Forest has been applied to the microarray dataset classification. In the framework of Rotation Forest, a linear transformation method is required to project data into new feature space for each classifier, and then the base classifiers are trained in different new spaces so as to enhance both the accuracies of base classifiers and the diversity in the ensemble system. Principal component analysis (PCA), non-parametric discriminant analysis (NDA) and random projections (RP) were applied to feature transformation in the original Rotation Forest. In this paper, we use independent component analysis (ICA) as a new transformation method since it can better describe the property of microarray data. The breast cancer dataset and prostate dataset are deployed to validate the efficiency of Rotation Forest. In all the experiments, it can be found that Rotation Forest outperforms other MCSs, such as Bagging and Boosting. In addition, the experimental results also revealed that ICA can further improve the performance of Rotation Forest compared with the original transformation methods.