Machine Learning
Machine Learning
Partially Supervised Classification of Text Documents
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
One-class svms for document classification
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
PEBL: Web Page Classification without Negative Examples
IEEE Transactions on Knowledge and Data Engineering
Sparseness of support vector machines
The Journal of Machine Learning Research
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Consistency and Convergence Rates of One-Class SVMs and Related Algorithms
The Journal of Machine Learning Research
Sparseness vs Estimating Conditional Probabilities: Some Asymptotic Results
The Journal of Machine Learning Research
Kernel-based data fusion for gene prioritization
Bioinformatics
Learning classifiers from only positive and unlabeled data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Cool Blog Classification from Positive and Unlabeled Examples
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Learning to classify texts using positive and unlabeled data
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Maximum margin clustering made practical
IEEE Transactions on Neural Networks
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A Neyman-Pearson approach to statistical learning
IEEE Transactions on Information Theory
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We consider the problem of learning a binary classifier from a training set of positive and unlabeled examples, both in the inductive and in the transductive setting. This problem, often referred to as PU learning, differs from the standard supervised classification problem by the lack of negative examples in the training set. It corresponds to an ubiquitous situation in many applications such as information retrieval or gene ranking, when we have identified a set of data of interest sharing a particular property, and we wish to automatically retrieve additional data sharing the same property among a large and easily available pool of unlabeled data. We propose a new method for PU learning with a conceptually simple implementation based on bootstrap aggregating (bagging) techniques: the algorithm iteratively trains many binary classifiers to discriminate the known positive examples from random subsamples of the unlabeled set, and averages their predictions. We show theoretically and experimentally that the method can match and even outperform the performance of state-of-the-art methods for PU learning, particularly when the number of positive examples is limited and the fraction of negatives among the unlabeled examples is small. The proposed method can also run considerably faster than state-of-the-art methods, particularly when the set of unlabeled examples is large.