Intrusion Detection is an invaluable part of computer networks defense. An important consideration is the fact that raising false alarms carries a significantly lower cost than not detecting attacks. For this reason, we examine how cost-sensitive classification methods can be used in Intrusion Detection systems. The performance of the approach is evaluated under different experimental condition...

Embedding cost-sensitive factors into the classifiers increases the classification stability and reduces the classification costs for classifying high-scale, redundant, and imbalanced datasets, such as the gene expression data. In this study, we extend our previous work, that is, Dissimilar ELM (D-ELM), by introducing misclassification costs into the classifier. We name the proposed algorithm a...

We propose a novel cost-sensitive multi-label classification algorithm called cost-sensitive random pair encoding (CSRPE). CSRPE reduces the costsensitive multi-label classification problem to many cost-sensitive binary classification problems through the label powerset approach followed by the classic oneversus-one decomposition. While such a näıve reduction results in exponentiallymany classi...

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...

biomedical datasets usually include a large number of features relative to the number of samples. however, some data dimensions may be less relevant or even irrelevant to the output class. selection of an optimal subset of features is critical, not only to reduce the processing cost but also to improve the classification results. to this end, this paper presents a hybrid method of filter and wr...

Loan fraud is a critical factor in the insolvency of financial institutions, so companies make an effort to reduce the loss from fraud by building a model for proactive fraud prediction. However, there are still two critical problems to be resolved for the fraud detection: (1) the lack of cost sensitivity between type I error and type II error in most prediction models, and (2) highly skewed di...

We propose a novel approach that reduces cost-sensitive classification to one-sided regression. The approach stores the cost information in the regression labels and encodes the minimum-cost prediction with the onesided loss. The simple approach is accompanied by a solid theoretical guarantee of error transformation, and can be used to cast any one-sided regression method as a costsensitive cla...

In many real-world scenarios, it is necessary to make judgments at differing levels of granularity due to computational constraints. Particularly when there are a large number of classifications that must be done in a real-time streaming setting and there is a significant difference in the time required to acquire different subsets of features, it is important to have an intelligent strategy fo...