Cost-sensitive learning is a popular approach to addressing the problem of class imbalance for many classification algorithms in machine learning. However, most cost-sensitive algorithms are dependent on manually designed cost matrices. Unfortunately, in many cases, it is often not easy for humans, even experts, to accurately specify misclassification costs for different mistakes due to the lack of domain knowledge related to actual situations in some complex unbalanced problems. As a result, these cost-sensitive algorithms cannot be directly applied. This paper proposes a new genetic programmingbased approach to developing cost-sensitive classifiers that are independent of manually designed cost matrices. The proposed method is able to construct classifiers and learn cost intervals automatically and simultaneously. In the proposed method, a tree representation, terminal sets and function sets are designed and developed. We examine the effectiveness of the proposed method on ten high-dimensional unbalanced datasets. The experimental results show that the proposed method often outperforms compared methods for highdimensional unbalanced classification. Furthermore, according to the analysis of evolved trees, the constructed classifiers often only need a small number of features to achieve a good classification performance.

中文翻译：

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通过遗传编程为二元高维不平衡分类开发基于区间的成本敏感分类器 [研究前沿]

成本敏感学习是解决机器学习中许多分类算法的类不平衡问题的流行方法。然而，大多数成本敏感算法都依赖于手动设计的成本矩阵。遗憾的是，在很多情况下，由于在一些复杂的不平衡问题中缺乏与实际情况相关的领域知识，人类甚至专家往往不容易准确指定不同错误的误分类成本。因此，这些成本敏感的算法不能直接应用。本文提出了一种新的基于遗传编程的方法来开发独立于手动设计的成本矩阵的成本敏感分类器。所提出的方法能够自动且同时地构建分类器并学习成本区间。在提出的方法中，设计和开发了树表示、终端集和功能集。我们检查了所提出的方法在十个高维不平衡数据集上的有效性。实验结果表明，所提出的方法通常优于高维不平衡分类的比较方法。此外，根据对进化树的分析，构建的分类器往往只需要少量特征即可达到良好的分类性能。