Classical 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 present a natural cost-sensitive extension of the...

Active Learning (AL) can be defined as a selectively supervised learning protocol intended to present those data to an oracle for labeling which will be most enlightening for machine learning. While AL traditionally accounts for the value of the information obtained, it often ignores the cost of obtaining the information thus causing it to perform sub-optimally with respect to total cost. We pr...

Existing supervised learning techniques can support product recommendations but are ineffective in scenarios characterized by single-class learning; i.e., training samples consisted of some positive examples and a much greater number of unlabeled examples. To address the limitations inherent in existing single-class learning techniques, we develop COst-sensitive Learning-based Positive Example ...

This paper introduces gain-cost classi…cation matrix for targeting customer groups. A real life application was realized on a customer database of CCF Bank containing more than 400,000 instances. Results shows that scoring is almost insensitive to di¤erent cost-gain hypothesis; on the other hand, the optimal targeted group size and the expected global bene…t rely to cost-gain hypothesis. Keywor...

Feature selection is an essential process for machine learning tasks since it improves generalization capabilities, and reduces run-time and amodel’s complexity. Inmany applications, the cost of collecting the features must be taken into account. To cope with the cost problem, we developed a new cost-sensitive fitness function based on histogram comparison. This function is integrated with a ge...

Because of the unbalanced class and skewed profit distribution in customer purchase data, the unknown and variant costs of false negative errors are a common problem for predicting the high-value customers in marketing operations. Incorporating cost-sensitive learning into forecasting models can improve the return on investment under resource constraint. This study proposes a cost-sensitive lea...

Outsourced software project is one of the main ways of software development, which is of high failure rate. Intelligent risk prediction model can help identify high risk project in time. However, the existing models are mostly based on such a hypothesis that all the cost of misclassification is equal, which is not consistent with the reality that in the domain of software project risk predictio...

On Structured Output Training: Hard Cases and an Efficient Alternative p. 7 Spares Kernel SVMs via Cutting-Plane Training p. 8 Hybrid Least-Squares Algorithms for Approximate Policy Evaluation p. 9 A Self-training Approach to Cost Sensitive Uncertainty Sampling p. 10 Learning Multi-linear Representations of Distributions for Efficient Inference p. 11 Cost-Sensitive Learning Based on Bregman Div...

Classification is a data mining technique which is utilized to predict the future by using available data and aims to discover hidden relationships between variables and classes. Since the cost component is crucial in most real life classification problems and most traditional classification methods work for the purpose of correct classification, developing cost-sensitive classifiers which mini...