A Hybrid Random Subspace Classifier Fusion Approach for Protein Mass Spectra Classification

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 convention...

• Type: Conference
• Year: 2008
• Where: EVOW
• Authors: Amin Assareh, Mohammad Hassan Moradi, L. Gwenn Volkert
• Keywords: Artificial Intelligence, Classifier Combination, Classifier Fusion Strategies, EVOW 2008, Random Subspace