In this paper, we perform an empirical study of the impact of noise on cost-sensitive (CS) learning, through observations on how a CS learner reacts to the mislabeled training examples in terms of misclassification cost and classification accuracy. Our empirical results and theoretical analysis indicate that mislabeled training examples can raise serious concerns for cost-sensitive classificati...

In recent years, the algorithms of learning to rank have been proposed by researchers. However, in information retrieval, instances of ranks are imbalanced. After the instances of ranks are composed to pairs, the pairs of ranks are imbalanced too. In this paper, a cost-sensitive risk minimum model of pairwise learning to rank imbalanced data sets is proposed. Following this model, the algorithm...

Nowadays in communities of Data Mining and Machine Learning, cost-sensitive classification and online learning have been widely examined. Even though these topics are getting more and more attention, very few studies are based on an important concern of Cost-Sensitive Online Classification. This problem can be explored widely and new technique can be implemented to deal with this issue. By dire...

Deep learning algorithms have seen a massive rise in popularity for remote sensing over the past few years. Recently, studies on applying deep techniques to graph data (e.g., public transport networks) been conducted. In node classification tasks, traditional neural network (GNN) models assume that different types of misclassifications an equal loss and thus seek maximize posterior probability ...

In order to lower the classification cost and improve the performance of the classifier, this paper proposes the approach of the dynamic cost-sensitive ensemble classification based on extreme learning machine for imbalanced massive data streams (DCECIMDS). Firstly, this paper gives the method of concept drifts detection by extracting the attributive characters of imbalanced massive data stream...

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-se...

Cost-sensitive classification is one of the hottest research topics in data mining and machine learning. It is prevalent in many applications, such as Automatic Target Recognition (ATR), medical diagnosis, etc. However, the data in practice may be inconsistent due to dimensional reduction operation or other pre-processing, yet it is not clear how the inconsistent data affects cost-sensitive lea...

Motivated by applications in the health insurance industry, we consider a seller who designs and sells a set of vertically differentiated products to a population of quality-sensitive customers. The seller’s business environment entails an uncertainty about production costs. We characterize the seller’s optimal price-quality schedule in the cases of: (a) static cost uncertainty, and (b) dynamic...