In this paper, a new hybrid methodology is introduced to design a cost-sensitive fuzzy rule-based classification system. A novel cost metric is proposed based on the combination of three different concepts: Entropy, Gini index and DKM criterion. In order to calculate the effective cost of patterns, a hybrid of fuzzy c-means clustering and particle swarm optimization algorithm is utilized. This ...

heretofore, a vast spectrum of pattern recognition methods has been considered by researchers to design credit scoring models. but what has not been considered enough in these studies is the lack of consideration of the needs of this subject which differentiate it from a simple classification. it is necessary to observe the costs of a wrong classification, because practically the cost of improp...

We present an efficient and effective method which extends the Boosting family of classifiers to allow the weighted classes. Typically classifiers do not treat individual classes separately. For most real world applications, this is not the case, not all classes have the same importance. The accuracy of a particular class can be more critical than others. In this paper we extend the mathematica...

In many classification settings, mistakes incur different application-dependent penalties based on the predicted and actual class labels. Costsensitive classifiers minimizing these penalties are needed. We propose a robust minimax approach for producing classifiers that directly minimize the cost of mistakes as a convex optimization problem. This is in contrast to previous methods that minimize...

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...