Data available in software engineering for many applications contains variability and it is not possible to say which variable helps the process of prediction. Most work present defect prediction focused on selection best techniques. For this purpose, deep learning ensemble models have shown promising results. In contrast, there are very few researches that deals with cleaning training data par...

We study active feature selection, a novel selection setting in which unlabeled data is available, but the budget for labels limited, and examples to label can be actively selected by algorithm. focus on using classical mutual information criterion, selects k features with largest label. In setting, goal use significantly fewer than set size still find whose based entire large. explain experime...