基于凸包的ROC性能最大化多目标遗传编程
Convex Hull-Based Multi-objective Genetic Programming for Maximizing ROC Performance
摘要：
在数据采掘中，ROC通常用于分析分类器的性能。ROC凸包（ROCCH）是经验ROC曲线的最小凸优集（LCM），它覆盖了给定分类器集的潜在最优解。通常，ROC性能最大化可以被认为是最大化ROCCH，这也意味着最大化ROC空间中每个分类器的真阳性率（tpr）并最小化假阳性率（fpr）。然而，在ROCCH优化过程中，tpr和fpr彼此冲突。ROCCH最大化问题虽然看起来像是一个多目标优化问题，但其专用字符的特殊性使其不同于传统的多目标优化问题。在这项工作中，我们将讨论它们之间的差异，并提出基于凸包的多目标遗传编程（CH-MOGP）来解决ROCCH最大化问题。基于凸包的排序是一种基于指标的选择方案，其目的是最大化凸包下的面积，该面积用作一组点的性能的一元指标。一个选择过程中描述，可以有效地实现，并遵循类似的设计原则比经典的超体积为基础的优化算法。据推测，通过使用定制的基于指标的选择方案CH-MOGP得到更有效的ROC凸包近似比算法，计算所有的Pareto最优点。为了验证我们的假设，我们比较了新的CH-MOGP与MOGP与经典的选择方案，包括NSGA-II，MOEA/D）和SMS-EMOA。同时，将CH-MOGP与C4.5、Naive Bayes、Prie等传统机器学习算法进行了比较。基于22个著名UCI数据集的实验结果表明，CH-MOGP的性能明显优于传统的EMOA。
英文摘要：
ROC is usually used to analyze the performance of classifiers in data mining. ROC convex hull (ROCCH) is the least convex major-ant (LCM) of the empirical ROC curve, and covers potential optima for the given set of classifiers. Generally, ROC performance maximization could be considered to maximize the ROCCH, which also means to maximize the true positive rate (tpr) and minimize the false positive rate (fpr) for each classifier in the ROC space. However, tpr and fpr are conflicting with each other in the ROCCH optimization process. Though ROCCH maximization problem seems like a multi-objective optimization problem (MOP), the special characters make it different from traditional MOP. In this work, we will discuss the difference between them and propose convex hull-based multi-objective genetic programming (CH-MOGP) to solve ROCCH maximization problems. Convex hull-based sort is an indicator based selection scheme that aims to maximize the area under convex hull, which serves as a unary indicator for the performance of a set of points. A selection procedure is described that can be efficiently implemented and follows similar design principles than classical hyper-volume based optimization algorithms. It is hypothesized that by using a tailored indicator-based selection scheme CH-MOGP gets more efficient for ROC convex hull approximation than algorithms which compute all Pareto optimal points. To test our hypothesis we compare the new CH-MOGP to MOGP with classical selection schemes, including NSGA-II, MOEA/D) and SMS-EMOA. Meanwhile, CH-MOGP is also compared with traditional machine learning algorithms such as C4.5, Naive Bayes and Prie. Experimental results based on 22 well-known UCI data sets show that CH-MOGP outperforms significantly traditional EMOAs.
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。
