基于类比的努力估计的集合调整方法的经验评估
An empirical evaluation of ensemble adjustment methods for analogy-based effort estimation
摘要：
目的：本文探讨集成学习的潜力，用于调整方法的变种，基于类比的努力估计。k的类比被使用的数量也进行了研究。方法我们进行了一个大规模的比较研究，其中许多合奏构造n出40个可能的有效的调整方法的变体被应用到8个数据集。基于标准化准确度和效应量评价每种方法的性能。结果如下：结果进行了统计显著性检验，并显示出合理的显着改善的预测性能，集成方法的应用。结论：我们的结论表明，集成的调整方法可以很好地工作，并取得良好的性能，即使他们并不总是优于单一的方法。我们还建议仅使用线性调整方法构建集成，因为它们表现出更好的性能，并且经常排名较高。
英文摘要：
Objective: This paper investigates the potential of ensemble learning for variants of adjustment methods used in analogy-based effort estimation. The number k of analogies to be used is also investigated. Method We perform a large scale comparison study where many ensembles constructed from n out of 40 possible valid variants of adjustment methods are applied to eight datasets. The performance of each method was evaluated based on standardized accuracy and effect size. Results: The results have been subjected to statistical significance testing, and show reasonable significant improvements on the predictive performance where ensemble methods are applied. Conclusion: Our conclusions suggest that ensembles of adjustment methods can work well and achieve good performance, even though they are not always superior to single methods. We also recommend constructing ensembles from only linear adjustment methods, as they have shown better performance and were frequently ranked higher.
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