On the Best Search Method in the LEM1 and LEM2 Algorithms
On the Best Search Method in the LEM1 and LEM2 Algorithms
复制标题
论LEM1和LEM2算法中的最佳搜索方法
DOI：
10.1007/978-3-7908-1888-8_4
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发表时间：
1998
期刊：
影响因子：
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通讯作者：
Paolo Werbrouck
中科院分区：
文献类型：
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作者：
J. Grzymala;Paolo Werbrouck
本报告介绍了两种机器学习算法的实验结果：LEM1和LEM2。这两种算法都属于LEM（Learning from Examples Module）家族，LEM1算法采用了两种不同的属性依赖性测试方法：划分和下边界。通过对两种不同版本的算法进行开发，并在一组测试文件上比较了它们的运行时间，针对LEM2进行了大量的实验，以寻找描述空间的最佳搜索方法。在算法中使用的一些算法来挑选“最佳”的属性值对的规则的生成进行了选择和测试。不同的方法的质量进行了比较的基础上的条件的总数，规则的总数，和规则的平均长度。
This report presents results of experiments on two algorithms of machine learning: LEM1 and LEM2. Both algorithms belong to the LEM (Learning from Examples Module) family developed at the Department of Computer Science, University of Kansas.For LEM1, two different approaches to test attribute dependence were compared: partition and lower boundaries. The two different versions of the algorithm were developed and their run times on a set of test files were compared.For LEM2 a number of experiments were made to find the best search method of the description space. Some heuristics used within the algorithm to pick the “best” attribute-value pairs for the generation of rules were selected and tested. The quality of different methods has been compared on the basis of the total number of conditions, the total number of rules, and the average length of rules.