Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Enhancing Supervised Learning with Unlabeled Data
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Symmetric Nearest Neighbor Learning Rule
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Solving the Multiple-Instance Problem: A Lazy Learning Approach
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers
IEEE Transactions on Knowledge and Data Engineering
Semisupervised Regression with Cotraining-Style Algorithms
IEEE Transactions on Knowledge and Data Engineering
Analyzing Co-training Style Algorithms
ECML '07 Proceedings of the 18th European conference on Machine Learning
When Semi-supervised Learning Meets Ensemble Learning
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
TSFS: A Novel Algorithm for Single View Co-training
CSO '09 Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization - Volume 01
Semi-supervised learning with very few labeled training examples
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Semi-supervised learning by disagreement
Knowledge and Information Systems
Link prediction in complex networks based on cluster information
SBIA'12 Proceedings of the 21st Brazilian conference on Advances in Artificial Intelligence
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Semi-supervised learning is a machine learning paradigm in which the induced hypothesis is improved by taking advantage of unlabeled data. It is particularly useful when labeled data is scarce. Cotraining is a widely adopted semi-supervised approach that assumes availability of two views of the training data a restrictive assumption for most real world tasks. In this paper, we propose a one-view Cotraining approach that combines two different k-Nearest Neighbors (KNN) strategies referred to as global and local k-NN. In global KNN, the nearest neighbors selected to classify a new instance are given by the training examples which include this instance as one of their own k-nearest neighbors. In local KNN, on the other hand, the neighborhood considered when classifying a new instance is computed with the traditional KNN approach. We carried out experiments showing that a combination of these strategies significantly improves the classification accuracy in Cotraining, particularly when one single view of training data is available. We also introduce an optimized algorithm to cope with time complexity of computing the global KNN, which enables tackling real classification problems.