A genetic programming method for classifier construction and cost learning in high-dimensional unbalanced classification
GECCO '20: Genetic and Evolutionary Computation Conference Cancún Mexico July, 2020(2020)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络