The Strength of Weak Learnability
Machine Learning
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Journal of Biomedical Informatics
Bayesian Inference on Hidden Knowledge in High-Throughput Molecular Biology Data
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Guidelines to Select Machine Learning Scheme for Classification of Biomedical Datasets
EvoBIO '09 Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Fuzzy rule base classifier fusion for protein mass spectra based ovarian cancer diagnosis
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
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Classifier fusion strategies have shown great potential to enhance the performance of pattern recognition systems. There is an agreement among researchers in classifier combination that the major factor for producing better accuracy is the diversity in the classifier team. Re-sampling based approaches like bagging, boosting and random subspace generate multiple models by training a single learning algorithm on multiple random replicates or sub-samples, in either feature space or the sample domain. In the present study we proposed a hybrid random subspace fusion scheme that simultaneously utilizes both the feature space and the sample domain to improve the diversity of the classifier ensemble. Experimental results using two protein mass spectra datasets of ovarian cancer demonstrate the usefulness of this approach for six learning algorithms (LDA, 1-NN, Decision Tree, Logistic Regression, Linear SVMs and MLP). The results also show that the proposed strategy outperforms three conventional resampling based ensemble algorithms on these datasets.