Data competitions provide an efficient cost-effective way to obtain diverse solutions for challenging problems across a wide variety of applications. The competition leaderboard, by necessity, must combine multiple objectives into a single scoring formula to determine winners and allocate prize money. However, after the competition concludes, the host may wish to choose a best solution for a particular scenario that focuses on only a subset of all the competition objectives. Through the use of Pareto fronts and graphical summaries, we describe how top solutions for a specific scenario can be identified and compared. The strategy uses intentional tie-handling, thresholds to eliminate undesirable solutions and Pareto fronts to identify objectively superior solutions for a subset of objectives. Then the strengths and weaknesses of different alternatives can be compared to find the ideal solution for the problem. The methods are illustrated with a real Topcoder data competition hosted by Los Alamos National Laboratory that used 16 different objectives to evaluate the quality of solutions for urban radiation search.

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了解赢得针对不同目标集的数据竞争解决方案的优点

数据竞赛提供了一种经济高效的方式，可以为各种应用程序中的挑战性问题获得不同的解决方案。根据需要，比赛排行榜必须将多个目标组合成一个单一的评分公式，以确定获胜者并分配奖金。但是，在比赛结束后，主办方可能希望为特定场景选择最佳解决方案，该方案仅关注所有比赛目标的一个子集。通过使用帕累托前沿和图形摘要，我们描述了如何识别和比较特定场景的最佳解决方案。该策略使用有意的平局处理、阈值来消除不需要的解决方案，并使用帕累托前沿来确定目标子集的客观上优解决方案。然后可以比较不同替代方案的优缺点，以找到问题的理想解决方案。这些方法通过由洛斯阿拉莫斯国家实验室主办的真实 Topcoder 数据竞赛来说明，该竞赛使用 16 个不同的目标来评估城市辐射搜索解决方案的质量。