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Classification methods has become increasingly popular for biomedical and bioinformatical data analysis. However, due to the difficulty of data acquisition, sometimes we could only obtain small-scale datasets which may leads to unreasonable generalization performances. For SVM-like algorithms, we could resort to Large Margin theory to find out solutions for such dilemma. Recent studies on large margin...
Information fusion is a research domain that strives to establish theories that exploit and analyze the data retrieved from multiple sources. Generally, these fusion theories try to combine these data for a classification task and to make the decision efficiently. The possibility theory is one of the most known in the information fusion domain. So, the possibility distribution estimation step represents...
Diversity is deemed to be a key issue in classifier combination. For this reason, not every classifier is an expert for every query pattern. Thus, many researchers have focused on dynamic ensemble selection. Most works, however, use only one criterion to perform the dynamic selection. Hence, multiple criteria can provide a decision more effective than the one produced by any of the criteria. Another...
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