Semi-supervised learning with an imperfect supervisor
Knowledge and Information Systems
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: A Knowledge Discovery Approach
Data Mining: A Knowledge Discovery Approach
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Semi-Supervised Learning (Adaptive Computation and Machine Learning)
Applied Soft Computing
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Label Propagation through Linear Neighborhoods
IEEE Transactions on Knowledge and Data Engineering
Semi-supervised Classification from Discriminative Random Walks
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
A Generic Local Algorithm for Mining Data Streams in Large Distributed Systems
IEEE Transactions on Knowledge and Data Engineering
A Survey of Uncertain Data Algorithms and Applications
IEEE Transactions on Knowledge and Data Engineering
Semi-supervised classification using local and global regularization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Semi-supervised learning with explicit misclassification modeling
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Handwritten Data Clustering Using Agents Competition in Networks
Journal of Mathematical Imaging and Vision
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Semisupervised learning is a machine learning approach which is able to employ both labeled and unlabeled samples in the training process. It is an important mechanism for autonomous systems due to the ability of exploiting the already acquired information and for exploring the new knowledge in the learning space at the same time. In these cases, the reliability of the labels is a crucial factor, because mislabeled samples may propagate wrong labels to a portion of or even the entire data set. This paper has the objective of addressing the error propagation problem originated by these mislabeled samples by presenting a mechanism embedded in a network-based (graph-based) semisupervised learning method. Such a procedure is based on a combined random-preferential walk of particles in a network constructed from the input data set. The particles of the same class cooperate among them, while the particles of different classes compete with each other to propagate class labels to the whole network. Computer simulations conducted on synthetic and real-world data sets reveal the effectiveness of the model.