The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Ensembling neural networks: many could be better than all
Artificial Intelligence
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Classification by evolutionary ensembles
Pattern Recognition
Ensemble learning for independent component analysis
Pattern Recognition
How to stop the evolutionary process in evolving neural network ensembles
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Designing classifier fusion systems by genetic algorithms
IEEE Transactions on Evolutionary Computation
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Bagging Constraint Score for feature selection with pairwise constraints
Pattern Recognition
Partition-conditional ICA for Bayesian classification of microarray data
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
The design of evolutionary multiple classifier system for the classification of microarray data
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
WSEAS Transactions on Information Science and Applications
An ensemble of SVM classifiers based on gene pairs
Computers in Biology and Medicine
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Independent component analysis (ICA) has been widely used to tackle the microarray dataset classification problem, but there still exists an unsolved problem that the independent component (IC) sets may not be reproducible after different ICA transformations. Inspired by the idea of ensemble feature selection, we design an ICA based ensemble learning system to fully utilize the difference among different IC sets. In this system, some IC sets are generated by different ICA transformations firstly. A multi-objective genetic algorithm (MOGA) is designed to select different biologically significant IC subsets from these IC sets, which are then applied to build base classifiers. Three schemes are used to fuse these base classifiers. The first fusion scheme is to combine all individuals in the final generation of the MOGA. In addition, in the evolution, we design a global-recording technique to record the best IC subsets of each IC set in a global-recording list. Then the IC subsets in the list are deployed to build base classifier so as to implement the second fusion scheme. Furthermore, by pruning about half of less accurate base classifiers obtained by the second scheme, a compact and more accurate ensemble system is built, which is regarded as the third fusion scheme. Three microarray datasets are used to test the ensemble systems, and the corresponding results demonstrate that these ensemble schemes can further improve the performance of the ICA based classification model, and the third fusion scheme leads to the most accurate ensemble system with the smallest ensemble size.