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In active learning algorithms, informative samples are usually queried for true labels according to the disagreement of existing hypotheses. However we observed that, when the streaming dataset has skewed class membership, the imbalanced data classification problem is caused in active learning. The Minority class is overwhelmed by the majority class in generating the hypotheses. In this paper, for...
Multitask network structure learning is an important problem in several scientific domains, such as, computational neuroscience and bioinformatics. However, existing algorithms do not leverage valuable domain knowledge about the relatedness of tasks. We present the first multitask Bayesian network learning algorithm that incorporates task-relatedness. Empirical results demonstrate that our algorithm...
During recent years, active learning has evolved into a popular paradigm for utilizing user's feedback to improve accuracy of learning algorithms. Active learning works by selecting the most informative sample among unlabeled data and querying the label of that point from user. Many different methods such as uncertainty sampling and minimum risk sampling have been utilized to select the most informative...
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