Wenbin Pei
ABSTRACT
Cost-sensitive learning has been widely used to address the problem of class imbalance. However, cost matrices are often manually designed. In many real-world applications, cost values are often unknown because of the limited domain knowledge. This paper proposes a new genetic programming method to construct cost-sensitive classifiers, which do not require the manually designed cost values. The experimental results show that the proposed method often outperforms existing GP methods.
- Urvesh Bhowan, Mark Johnston, and Mengjie Zhang. 2012. Developing new fitness functions in genetic programming for classification with unbalanced data. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 42, 2 (2012), 406--421.Google Scholar
Digital Library
- Charles Elkan. 2001. The foundations of cost-sensitive learning. In International joint conference on artificial intelligence, Vol. 17. Lawrence Erlbaum Associates Ltd, 973--978.Google ScholarDigital Library
Index Terms
- A genetic programming method for classifier construction and cost learning in high-dimensional unbalanced classification
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