代价敏感分类：混合遗传决策树归纳算法的实证评估
Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm
摘要：
本文介绍了一种新的代价敏感分类算法ICET。ICET使用遗传算法来进化决策树归纳算法的偏差群体。遗传算法的适应度函数是当使用决策树时分类的平均成本，包括测试（特征、测量）的成本和分类错误的成本。ICET在这里与其他三种成本敏感的分类算法-EG 2，CS-ID 3和IDX -以及C4.5进行了比较，C4.5在不考虑成本的情况下进行分类。这五种算法在五个真实世界的医疗数据集上进行了经验评估。进行了三组实验。第一组检查了五种算法在五个数据集上的基线性能，并确定ICET的性能明显优于其竞争对手。第二组测试ICET在各种条件下的鲁棒性，并显示ICET保持其优势。第三组着眼于ICET在偏置空间中的搜索，并发现了一种改进搜索的方法。
英文摘要：
This paper introduces ICET, a new algorithm for cost-sensitive classification. ICET uses a genetic algorithm to evolve a population of biases for a decision tree induction algorithm. The fitness function of the genetic algorithm is the average cost of classification when using the decision tree, including both the costs of tests (features, measurements) and the costs of classification errors. ICET is compared here with three other algorithms for cost-sensitive classification - EG2, CS-ID3, and IDX - and also with C4.5, which classifies without regard to cost. The five algorithms are evaluated empirically on five real-world medical datasets. Three sets of experiments are performed. The first set examines the baseline performance of the five algorithms on the five datasets and establishes that ICET performs significantly better than its competitors. The second set tests the robustness of ICET under a variety of conditions and shows that ICET maintains its advantage. The third set looks at ICET's search in bias space and discovers a way to improve the search.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
