To prevent an exploit, the security analyst must implement a suitable countermeasure. In this paper, we consider cost-sensitive attack graphs (CAGs) for network vulnerability analysis. In these attack graphs, a weight is assigned to each countermeasure to represent the cost of its implementation. There may be multiple countermeasures with different weights for preventing a single exploit. Also,...

Many real-world classification problems come with costs which can vary for different types of misclassification. It is thus important to develop cost-sensitive classifiers minimize the total misclassification cost. Although binary have been well-studied, solving multicategory still challenging. A popular approach address this issue construct K functions a K-class problem and remove redundancy b...

In many real-life decision making situations the default assumption of equal (mis-)classification costs underlying pattern recognition techniques is most likely violated. Consider the case of insurance claim fraud detection for which an early claim screening facility is to be built to decide upon the nature of an incoming claim as either suspicious or not. This decision typically forms the basi...

Many studies on the cost-sensitive learning assumed that a unique cost matrix is known for a problem. However, this assumption may not hold for many real-world problems. For example, a classifier might need to be applied in several circumstances, each of which associates with a different cost matrix. Or, different human experts have different opinions about the costs for a given problem. Motiva...

Many real-world datasets for machine learning and data mining contain missing values, and much previous research regards it as a problem, and attempts to impute missing values before training and testing. In this paper, we study this issue in cost-sensitive learning that considers both test costs and misclassification costs. If some attributes (tests) are too expensive in obtaining their values...

The problem of class imbalance along with classoverlapping has become a major issue in the domain of supervised learning. Most supervised learning algorithms assume equal cardinality of the classes under consideration while optimizing the cost function and this assumption does not hold true for imbalanced datasets which results in sub-optimal classification. Therefore, various approaches, such ...

In many practical reinforcement learning problems, the state space is too large to permit an exact representation of the value function, much less the time required to compute it. In such cases, a common solution approach is to compute an approximation of the value function in terms of state features. However, relatively little attention has been paid to the cost of computing these state featur...

The existing classifiers for uncertain data don’t consider the dynamic cost, so this paper proposes the classification approach of the dynamic cost-sensitive decision tree for uncertain data based on the genetic algorithm (GDCDTU) , which overcomes the limitations of the stationary cost, and searches automatically the suitable cost space of every sub datasets. Firstly, this paper gives the dyna...

real-world predictive data mining (classification or regression) problems are often cost sensitive, meaning that different types of prediction errors are not equally costly. While cost-sensitive learning methods for classification problems have been extensively studied recently, cost-sensitive regression has not been adequately addressed in the data mining literature yet. In this paper, we firs...

This study is concerned with whether it is possible to detect what information contained in the training data and background knowledge is relevant for solving the learning problem, and whether irrelevant information can be eliminated in preprocessing before starting the learning process. A case study of data preprocessing for a hybrid genetic algorithm shows that the elimination of irrelevant f...