Evolutionary learning of hierarchical decision rules
Evolutionary learning of hierarchical decision rules
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DOI：
10.1109/tsmcb.2002.805696
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发表时间：
2003-04-01
影响因子：
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通讯作者：
Toro, M
中科院分区：
文献类型：
--
作者：
Aguilar-Ruiz, JS;Riquelme, JC;Toro, M
本文介绍了一种基于进化算法的方法，层次决策规则（HIDER），学习规则在连续和离散领域。该算法产生一个分层的规则集，也就是说，规则是顺序获得的，因此，必须按顺序尝试，直到找到一个满足条件的规则。因此，可以减少规则的数量，因为规则可以在彼此内部。进化算法对种群中的个体使用真实的和二进制编码。我们已经测试了我们的系统上的真实的数据从UCI存储库，和十倍交叉验证的结果进行了比较C4.5s，C4.5Rules，See5s和See5Rules。实验结果表明，HIDER算法在实际应用中取得了良好的效果。
This paper describes an approach based on evolutionary algorithms, hierarchical decision rules (HIDER), for learning rules in continuous and discrete domains. The algorithm produces a hierarchical set of rules, that is, the rules are sequentially obtained and must be, therefore, tried in order until one is found whose conditions are satisfied. Thus, the number of rules may be reduced because the rules could be inside one another. The evolutionary algorithm uses both real and binary coding for the individuals of the population. We have tested our system on real data from the UCI Repository, and the results of a ten-fold cross-validation are compared to C4.5s, C4.5Rules, See5s, and See5Rules. The experiments show that HIDER works well in practice.