Machine Learning - Special issue on learning with probabilistic representations
Partially Supervised Classification of Text Documents
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
PEBL: positive example based learning for Web page classification using SVM
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Transforming classifier scores into accurate multiclass probability estimates
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Building Text Classifiers Using Positive and Unlabeled Examples
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Learning Conditional Independence Tree for Ranking
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Learning Weighted Naive Bayes with Accurate Ranking
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning
Augmenting naive Bayes for ranking
ICML '05 Proceedings of the 22nd international conference on Machine learning
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Full Bayesian network classifiers
ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning from positive and unlabeled examples
Theoretical Computer Science - Algorithmic learning theory (ALT 2000)
Learning Bayesian classifiers from positive and unlabeled examples
Pattern Recognition Letters
Learning classifiers from only positive and unlabeled data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
One-Class Classification of Text Streams with Concept Drift
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
A Novel Bayes Model: Hidden Naive Bayes
IEEE Transactions on Knowledge and Data Engineering
Learning to classify texts using positive and unlabeled data
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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This paper studies the problem of Positive Unlabeled learning (PU learning), where positive and unlabeled examples are used for training. Naive Bayes (NB) and Tree Augmented Naive Bayes (TAN) have been extended to PU learning algorithms (PNB and PTAN). However, they require user-specified parameter, which is difficult for the user to provide in practice. We estimate this parameter following [2] by taking the "selected completely at random" assumption and reformulate these two algorithms with this assumption. Furthermore, based on supervised algorithms Averaged One-Dependence Estimators (AODE), Hidden Naive Bayes (HNB) and Full Bayesian network Classifier (FBC), we extend these algorithms to PU learning algorithms (PAODE, PHNB and PFBC respectively). Experimental results on 20 UCI datasets show that the performance of the Bayesian algorithms for PU learning are comparable to corresponding supervised ones in most cases. Additionally, PNB and PFBC are more robust against unlabeled data, and PFBC generally performs the best.