Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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
Uniform object generation for optimizing one-class classifiers
The Journal of Machine Learning Research
Building Text Classifiers Using Positive and Unlabeled Examples
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Text classification from positive and unlabeled documents
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Splice site identification by idlBNs
Bioinformatics
Computer Methods and Programs in Biomedicine
Learning to classify texts using positive and unlabeled data
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Unsupervised Text Learning Based on Context Mixture Model with Dirichlet Prior
Advanced Web and NetworkTechnologies, and Applications
Feature subset selection from positive and unlabelled examples
Pattern Recognition Letters
OcVFDT: one-class very fast decision tree for one-class classification of data streams
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
Bayesian classifiers for positive unlabeled learning
WAIM'11 Proceedings of the 12th international conference on Web-age information management
A pairwise ranking based approach to learning with positive and unlabeled examples
Proceedings of the 20th ACM international conference on Information and knowledge management
Positive unlabeled learning for time series classification
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Learning from positive and unlabelled examples using maximum margin clustering
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Learning from data streams with only positive and unlabeled data
Journal of Intelligent Information Systems
Learning Bayesian network classifiers from label proportions
Pattern Recognition
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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 thus new algorithms are required. One of these positive unlabeled learning algorithms is the positive naive Bayes (PNB), which is an adaptation of the naive Bayes induction algorithm that does not require negative instances. In this work we propose two ways of enhancing this algorithm. On one hand, we have taken the concept behind PNB one step further, proposing a procedure to build more complex Bayesian classifiers in the absence of negative instances. We present a new algorithm (named positive tree augmented naive Bayes, PTAN) to obtain tree augmented naive Bayes models in the positive unlabeled domain. On the other hand, we propose a new Bayesian approach to deal with the a priori probability of the positive class that models the uncertainty over this parameter by means of a Beta distribution. This approach is applied to both PNB and PTAN, resulting in two new algorithms. The four algorithms are empirically compared in positive unlabeled learning problems based on real and synthetic databases. The results obtained in these comparisons suggest that, when the predicting variables are not conditionally independent given the class, the extension of PNB to more complex networks increases the classification performance. They also show that our Bayesian approach to the a priori probability of the positive class can improve the results obtained by PNB and PTAN.