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摘要翻译：
Web工作量估计是一个预测任何软件项目系统的工作量和成本的过程，包括金钱、进度和人员。在过去的三十年里，人们提出了许多估算模型，并认为它是预算编制、风险分析、项目规划和控制以及项目改进投资分析的必要条件。本文研究了模糊ID3决策树在软件成本估算中的应用；该模型将ID3决策树的原理和模糊集理论的概念相结合，使模型在描述软件项目时能够处理不确定和不精确的数据，从而大大提高了估计的准确性。采用MMRE和Pred作为本研究预测精度的度量。本文利用两个不同的软件项目数据集Tukutuku和COCOMO'81进行了一系列实验。并与ID3决策树的简明版本所得到的结果进行了比较。
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英文标题：
《Applying Fuzzy ID3 Decision Tree for Software Effort Estimation》
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作者：
Sanaa Elyassami and Ali Idri
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最新提交年份：
2011
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Software Engineering 软件工程
分类描述：Covers design tools, software metrics, testing and debugging, programming environments, etc. Roughly includes material in all of ACM Subject Classes D.2, except that D.2.4 (program verification) should probably have Logics in Computer Science as the primary subject area.
涵盖设计工具、软件度量、测试和调试、编程环境等。大致包括ACM所有主题课程D.2的材料，除了D.2.4（程序验证）可能应该有计算机科学中的逻辑作为主要主题领域。
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一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
Web Effort Estimation is a process of predicting the efforts and cost in terms of money, schedule and staff for any software project system. Many estimation models have been proposed over the last three decades and it is believed that it is a must for the purpose of: Budgeting, risk analysis, project planning and control, and project improvement investment analysis. In this paper, we investigate the use of Fuzzy ID3 decision tree for software cost estimation; it is designed by integrating the principles of ID3 decision tree and the fuzzy set-theoretic concepts, enabling the model to handle uncertain and imprecise data when describing the software projects, which can improve greatly the accuracy of obtained estimates. MMRE and Pred are used as measures of prediction accuracy for this study. A series of experiments is reported using two different software projects datasets namely, Tukutuku and COCOMO'81 datasets. The results are compared with those produced by the crisp version of the ID3 decision tree.
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PDF链接：
https://arxiv.org/pdf/1111.0158
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