Label noise is an important issue in classification, with many potential negative
consequences. For example, the accuracy of predictions may decrease, whereas the …

Accurately labeling training data plays a critical role in various supervised learning tasks.
Now a wide range of algorithms have been developed to identify and remove mislabeled …

Data are widely collected via the IoT for machine learning tasks in in-home health
monitoring applications and mislabeled training data lead to unreliable machine learning …

The simplest classification task is to divide a set of objects into two classes, but most of the
problems we find in real life applications are multi-class. There are many methods of …

Classification techniques are important in bioinformatics analysis as they can separate
various bioinformatical data into distinct groups. To obtain good classifiers, accurate labeling …

Accurately labeling training data plays a critical role in various supervised learning tasks.
Since labeling in practical applications might be erroneous due to various reasons, a wide …

Abstract Support Vector Machine (SVM) is a binary classifier, but most of the problems we
find in the real-life applications are multiclass. There are many methods of decomposition …

As a kind of noise, mislabeled training data exist in many applications. Because of their
negative effects on learning, many filter techniques have been proposed to identify and …

The error correcting output code (ECOC) technique is a genesral framework to solve the
multi-class problems using binary classifiers. The key problem in this approach is how to …

Accurately solving classification problems nowadays is likely to be the most relevant
machine learning task. Binary classification separating two classes only is algorithmically …