Add to ORKGAbstract. Learning algorithms from the fields of artificial neural networks and machine learning, typically, do not take any costs into account or allow only costs depending on the classes of the examples that are used for learning. As an extension of class dependent costs, we consider costs that are example, i.e. feature and class dependent. We derive a cost-sensitive perceptron learning rule for non-separable classes, that can be extended to multi-modal classes (DIPOL) and present a natural cost-sensitive extension of the support vector machine (SVM).
In many machine learning domains, misclassification costs are different for different examples, in t...
In this paper, we study cost-sensitive semi-supervised learning where many of the training examples ...
This paper explores the problem of learning from exam-ples when feature measurement costs are signif...
1 Learning algorithms from the fields of artificial neural networks and machine learning, typically,...
Cost-sensitive classification is one of mainstream research topics in data mining and machine learni...
There is a significant body of research in machine learning addressing techniques for performing cla...
Traditionally, classification algorithms aim to minimize the number of errors. However, this approac...
Abstract. We propose to study links between three important classification algorithms: Perceptrons, ...
Abstract. Existing cost-sensitive learning methods work with unequal misclassification cost that is ...
Abstract — This paper studies empirically the effect of sampling and threshold-moving in training co...
Abstract Real‐world classification often encounters a problem called class imbalance. When the data ...
Abstract—Many studies on the cost-sensitive learning assumed that a unique cost matrix is known for ...
It is an actual and challenging issue to learn cost-sensitive models from those datasets that are wi...
During the past decade, machine learning algorithms have become commonplace in large-scale real-worl...
In many machine learning domains, misclassification costs are different for different examples, in t...
In many machine learning domains, misclassification costs are different for different examples, in t...
In this paper, we study cost-sensitive semi-supervised learning where many of the training examples ...
This paper explores the problem of learning from exam-ples when feature measurement costs are signif...
1 Learning algorithms from the fields of artificial neural networks and machine learning, typically,...
Cost-sensitive classification is one of mainstream research topics in data mining and machine learni...
There is a significant body of research in machine learning addressing techniques for performing cla...
Traditionally, classification algorithms aim to minimize the number of errors. However, this approac...
Abstract. We propose to study links between three important classification algorithms: Perceptrons, ...
Abstract. Existing cost-sensitive learning methods work with unequal misclassification cost that is ...
Abstract — This paper studies empirically the effect of sampling and threshold-moving in training co...
Abstract Real‐world classification often encounters a problem called class imbalance. When the data ...
Abstract—Many studies on the cost-sensitive learning assumed that a unique cost matrix is known for ...
It is an actual and challenging issue to learn cost-sensitive models from those datasets that are wi...
During the past decade, machine learning algorithms have become commonplace in large-scale real-worl...
In many machine learning domains, misclassification costs are different for different examples, in t...
In many machine learning domains, misclassification costs are different for different examples, in t...
In this paper, we study cost-sensitive semi-supervised learning where many of the training examples ...
This paper explores the problem of learning from exam-ples when feature measurement costs are signif...