In practical applications of classification, there are often varying costs associated with different types of misclassification (e.g. fraud detection, anomaly detection and medical diagnosis), motivating the need for the so-called ”cost-sensitive” classification. In this paper, we introduce a family of novel boosting methods for cost-sensitive classification by applying the theory of gradient b...

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...

The classification of mineralized areas into different groups based on mineral grade and prospectivity is a practical problem in the area of optimal risk, time, and cost management of exploration projects. The purpose of this paper was to present a new approach for optimizing the grade classification model of an orebody. That is to say, through hybridizing machine learning with a metaheuristic ...

Computational cost of classification is as important as accuracy in on-line classification systems. The computational cost is usually dominated by the cost of computing implicit features of the raw input data. Very few efforts have been made to design classifiers which perform effectively with limited computational power; instead, feature selection is usually employed as a pre-processing step t...

Extremely unbalanced data and unequal costs are key challenges in data mining for campaign management in CRM. This paper presents an empirical study of cost-sensitive classification in a real-world campaign analysis in the newspaper industry, where the response rate is extremely low while the two types of misclassification costs are very different. Incorporating cost information provided by dom...