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In this paper, we propose a multi-class boosting method (multiBoost.imb) to address difficulties of learning from imbalanced data set as well as employment of stable base learners. A random resampling strategy is incorporated to diversify the training data set and to recover balance among all classes. Extending AdaBoost by adding an error adjustment parameter, early termination in the training phase...
More than a decade of research has produced numerous representations and similarity measures to support time series classification and clustering. Yet most of the work in the field is so focused on the representation or similarity measure that it ignores the possibility of improving performance using ensembles of representations or classifiers. This paper explores ways of exploiting representational...
Prediction of O-linked glycosylation sites in proteins is a challenging problem. In this paper, we introduced a new method to predict glycosylation sites in proteins. First, we built a Markov random field (MRF) to represent the sequence position relationship and model the underlying distribution of glycosylation sites. We then considered glycosylation site prediction as a class imbalance problem and...
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