Machine learning typically involves discovering regularities in a training set, then applying these learned regularities to classify objects in a test set. In this paper we presen...
We present a comprehensive suite of experimentation on the subject of learning from imbalanced data. When classes are imbalanced, many learning algorithms can suffer from the pers...
Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napol...
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to sem...
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal,...
In this paper we present the system ALLPAD for learning Logic Programs with Annotated Disjunctions (LPADs). ALLPAD modifies the previous system LLPAD in order to tackle real world ...
Active learning has been applied to different NLP tasks, with the aim of limiting the amount of time and cost for human annotation. Most studies on active learning have only simul...