Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
Weighted reduction for decision tables
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Fuzzy probabilistic approximation spaces and their information measures
IEEE Transactions on Fuzzy Systems
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In many real-world applications, the costs of different errors are often unequal. Therefore, the inclusion of costs into learning, also named cost-sensitive learning, has been regarded as one of the most relevant topics of future machine learning research. Rough set theory is a powerful mathematic tool dealing with inconsistent information for attribute dependence analysis, knowledge reduction and decision rule extraction. However, it is insensitive to the costs of misclassification due to the absence of a mechanism of considering the subjective knowledge. This paper discusses problems connected with introducing the subjective knowledge into rough set learning and proposes a weighted rough set approach for cost-sensitive learning. In this method, weights are employed to represent the subjective knowledge of costs and a weighted information system is defined firstly. With the introduction of weights, weighted attribute dependence analysis is carried out and an index of weighted approximate quality is given. Furthermore, weighted attribute reduction algorithm and weighted rule extraction algorithm are designed to find the reducts and rules with the consideration of weights. Based on the proposed weighted rough set, a series of comparing experimentations with several familiar general techniques on cost-sensitive learning are constructed. The results show that the approach of weighted rough set produces averagely the minimum misclassification costs and the lowest high cost errors.