Cited By
View all- Zhao YWang LZhu XJin TSun MLi X(2025)Probability knowledge acquisition from unlabeled instance based on dual learningKnowledge and Information Systems10.1007/s10115-024-02238-967:1(521-547)Online publication date: 1-Jan-2025
Numerous algorithms have been proposed to improve Naive Bayes (NB) by weakening its conditional attribute independence assumption, among which Tree Augmented Naive Bayes (TAN) has demonstrated remarkable classification performance in terms of ...
Many studies on ensemble learning that combines multiple classifiers have shown that, it is an effective technique to improve accuracy and stability of a single classifier. In this paper, we propose a novel discriminative classifier fusion method, which ...
The positive unlabeled learning term refers to the binary classification problem in the absence of negative examples. When only positive and unlabeled instances are available, semi-supervised classification algorithms cannot be directly applied, and ...
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