摘要翻译：
在本文中，我们提出了一种优化粗糙集划分大小的方法，并对HIV数据进行规则提取。利用遗传算法优化技术确定粗糙集的划分大小，以最大限度地提高粗糙集的预测精度。在南非产前调查获得的一组个人人口特征上测试了所提议的方法。分析中使用了6个人口学变量，这些变量分别是；种族、母亲年龄、教育程度、妊娠、生育和父亲年龄，结果或决定为艾滋病毒阳性或阴性。选择粗糙集理论是基于对抽取的规则易于解释的事实。等宽分区的预测精度为57.7%，优化分区后的预测精度为72.8%。另外几种方法也被用于HIV数据的分析，给出了它们的结果，并与粗糙集理论(RST)的结果进行了比较。
---
英文标题：
《Using Genetic Algorithms to Optimise Rough Set Partition Sizes for HIV
  Data Analysis》
---
作者：
Bodie Crossingham and Tshilidzi Marwala
---
最新提交年份：
2007
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
--
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。
--
一级分类：Quantitative Biology        数量生物学
二级分类：Quantitative Methods        定量方法
分类描述：All experimental, numerical, statistical and mathematical contributions of value to biology
对生物学价值的所有实验、数值、统计和数学贡献
--

---
英文摘要：
  In this paper, we present a method to optimise rough set partition sizes, to which rule extraction is performed on HIV data. The genetic algorithm optimisation technique is used to determine the partition sizes of a rough set in order to maximise the rough sets prediction accuracy. The proposed method is tested on a set of demographic properties of individuals obtained from the South African antenatal survey. Six demographic variables were used in the analysis, these variables are; race, age of mother, education, gravidity, parity, and age of father, with the outcome or decision being either HIV positive or negative. Rough set theory is chosen based on the fact that it is easy to interpret the extracted rules. The prediction accuracy of equal width bin partitioning is 57.7% while the accuracy achieved after optimising the partitions is 72.8%. Several other methods have been used to analyse the HIV data and their results are stated and compared to that of rough set theory (RST).
---
PDF链接：
https://arxiv.org/pdf/0705.2485