Semisupervised Regression with Cotraining-Style Algorithms
IEEE Transactions on Knowledge and Data Engineering
Traffic classification using en-semble learning and co-training
AIC'08 Proceedings of the 8th conference on Applied informatics and communications
Semi-supervised document retrieval
Information Processing and Management: an International Journal
Semi-supervised Learning with Multimodal Perturbation
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Recruiter selection model and implementation within the united states army
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
New Labeling Strategy for Semi-supervised Document Categorization
KSEM '09 Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management
Semi-supervised Classification Based on Clustering Ensembles
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
A data-driven approach to manage the length of stay for appendectomy patients
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Random relevant and non-redundant feature subspaces for co-training
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Semi-supervised learning applied to large data sets with very few labeled examples
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Co-training with relevant random subspaces
Neurocomputing
Expert Systems with Applications: An International Journal
A classification algorithm based on local cluster centers with a few labeled training examples
Knowledge-Based Systems
Question classification based on co-training style semi-supervised learning
Pattern Recognition Letters
Simple semi-supervised training of part-of-speech taggers
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
Semi-supervised dependency parsing using generalized tri-training
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Combining committee-based semi-supervised learning and active learning
Journal of Computer Science and Technology
A refinement approach to handling model misfit in semi-supervised learning
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Software defect detection with rocus
Journal of Computer Science and Technology
A new co-training-style random forest for computer aided diagnosis
Journal of Intelligent Information Systems
Diverse reduct subspaces based co-training for partially labeled data
International Journal of Approximate Reasoning
Computer Methods and Programs in Biomedicine
Combining active learning and semi-supervised for improving learning performance
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Sample-based software defect prediction with active and semi-supervised learning
Automated Software Engineering
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
DCPE co-training for classification
Neurocomputing
Unlabeled data and multiple views
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Online semi-supervised ensemble updates for fMRI data
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
A semi-supervised feature ranking method with ensemble learning
Pattern Recognition Letters
Exploiting unlabeled data to enhance ensemble diversity
Data Mining and Knowledge Discovery
Inter-training: Exploiting unlabeled data in multi-classifier systems
Knowledge-Based Systems
Web page and image semi-supervised classification with heterogeneous information fusion
Journal of Information Science
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Pattern classification and clustering: A review of partially supervised learning approaches
Pattern Recognition Letters
Information Sciences: an International Journal
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In computer-aided diagnosis (CAD), machine learning techniques have been widely applied to learn a hypothesis from diagnosed samples to assist the medical experts in making a diagnosis. To learn a well-performed hypothesis, a large amount of diagnosed samples are required. Although the samples can be easily collected from routine medical examinations, it is usually impossible for medical experts to make a diagnosis for each of the collected samples. If a hypothesis could be learned in the presence of a large amount of undiagnosed samples, the heavy burden on the medical experts could be released. In this paper, a new semisupervised learning algorithm named Co-Forest is proposed. It extends the co-training paradigm by using a well-known ensemble method named Random Forest, which enables Co-Forest to estimate the labeling confidence of undiagnosed samples and easily produce the final hypothesis. Experiments on benchmark data sets verify the effectiveness of the proposed algorithm. Case studies on three medical data sets and a successful application to microcalcification detection for breast cancer diagnosis show that undiagnosed samples are helpful in building CAD systems, and Co-Forest is able to enhance the performance of the hypothesis that is learned on only a small amount of diagnosed samples by utilizing the available undiagnosed samples.