遗传算法优化HIV数据分析中的粗糙集划分
Using Genetic Algorithms to Optimise Rough Set Partition Sizes for HIV Data Analysis
摘要：
在本文中，我们提出了一种方法来优化粗糙集分区的大小，规则提取艾滋病毒数据进行。利用遗传算法优化技术来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。所提出的方法进行了测试，从南非产前调查获得的个人的一组人口统计特性。在分析中使用了六个人口统计学变量，这些变量是：种族，母亲年龄，教育，妊娠，产次和父亲年龄，结果或决定是HIV阳性或阴性。粗糙集理论的选择是基于这样一个事实，它是容易解释的提取规则。等宽仓分割的预测准确率为57.7%，而优化分区后达到的准确率为72.8%。其他几种方法已被用来分析艾滋病毒的数据和他们的结果进行了说明，并比较粗糙集理论（ESTA）。
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).
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