… \cleaning" the training data. We introduce a method for identifying mislabeled instances that
is … a general method that can be applied to a dataset before feeding it to a learning algorithm. …

This paper presents a new approach for identifying and eliminating mislabeled training
instances for supervised learning algorithms. The novelty of this approach lies in the using of …

… To this end, we propose a novel method that identifies mislabeled data simply by observing
a network’s training dynamics. Our method builds upon recent theoretical and empirical …

… -based approach to identify mislabeled samples from training dynamics, which trains label
… the timeseries data to identify the mislabeled samples in the training set. This trained noise …

… data. This paper focuses on mislabeled data, which is one of the main types of noisy data. A
… Automatic noise reduction (ANR) has been proposed to identify and remove noisy training …

… to identify and eliminate mislabeled training data. Teng [16,17] applied a procedure to identify
… approach, called ADE (automatic data enhancement), to correct mislabeled instances in a …

… We show that our approach can successfully identify mislabeled instances with label noise
both completely at random (independent of the class) and at random (where some classes …

… set of training instances, a clean training dataset is important. Unfortunately, real world data
is never … In this paper, a new approach is proposed to identify and correct mislabeled training …

… when training data are insufficient. In this work, we propose a novel framework to learn from
mislabeled training data … It identifies and eliminates mislabeled data prior to training. Thus, a …

… The problem of identifying mislabeled training examples has … developed for editing the
training data to obtain better classifiers. … to the training set and filtering the mislabeled examples …