Classification with label noise: a Markov chain sampling framework
Classification with label noise: a Markov chain sampling framework is a scholarly work, published in 2018 in ''Data Mining and Knowledge Discovery''. The main subjects of the publication include probabilistic logic, correctness, Markov chain, Markov chain Monte Carlo, artificial intelligence, probability distribution, Variable-order Markov model, noise, algorithm, active learning, Markov model, hidden Markov model, sampling, concept drift, machine learning, pattern recognition, computer science, and Bayesian probability. The authors propose a Markov chain sampling (MCS) framework that accurately identifies mislabeled instances and robustly learns effective classifiers.