The quality of software depends heavily on how accurately it works. The accuracy is determined by the fact that the less the software modules are defect prone, the more accurate the software will be. So software defect prediction which classifies software modules into defect prone and non-defect prone categories is an important area where a lot of research works are being done. Cost sensitive l...

Due to its importance in many applications, the incomplete data mining has received increasing attention in recent years, but there has been little study of the cost-sensitive classification on incomplete data. Therefore this paper proposes the dynamic costsensitive extreme learning machine for classification of incomplete data based on the deep imputation network (DCELMIDC). Firstly, we propos...

In this paper we propose a very simple, yet general and effective method to make any cost-insensitive classifiers (that can produce probability estimates) cost-sensitive. The method, called Thresholding, selects a proper threshold from training instances according to the misclassification cost. Similar to other cost-sensitive meta-learning methods, Thresholding can convert any existing (and fut...

Cost-sensitive specialization is a generic technique for misclassification cost sensitive induction. This technique involves specializing aspects of a classifier associated with high misclassification costs and generalizing those associated with low misclassification costs. It is widely applicable and simple to implement. It could be used to augment the effect of standard cost-sensitive inducti...

Some deep convolutional neural networks were proposed for time-series classification and class imbalanced problems. However, those models performed degraded and even failed to recognize the minority class of an imbalanced temporal sequences dataset. Minority samples would bring troubles for temporal deep learning classifiers due to the equal treatments of majority and minority class. Until rece...