Using evolutionary algorithms as instance selection for data reduction in KDD: An experimental study
Using evolutionary algorithms as instance selection for data reduction in KDD: An experimental study
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DOI：
10.1109/tevc.2003.819265
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发表时间：
2003-12-01
影响因子：
14.3
通讯作者：
Lozano, M
中科院分区：
文献类型：
--
作者：
Cano, JR;Herrera, F;Lozano, M
进化算法是自适应的，是基于自然进化的方法，可用于搜索和优化。由于数据库中知识发现中的数据约简可以看作是一个搜索问题，因此可以用进化算法来解决这一问题，本文对四种具有代表性的进化算法模型的性能进行了实证研究，其中考虑了两种不同的实例选择角度，即原型选择和训练集选择。本文将这些算法与其他非进化实例选择算法进行了比较。结果表明，进化的实例选择算法始终优于非进化的实例选择算法，其主要优点是：更好的实例约简率、更高的分类精度和更容易解释的模型。
Evolutionary algorithms are adaptive, methods based on natural evolution that may be used for search and optimization. As data reduction in knowledge discovery in databases (KDDs) can be viewed as a search problem, it could be solved using evolutionary algorithms (EAs).In this paper, we have carried out an empirical study of the performance of four representative EA models in which we have taken into account two different instance selection perspectives, the prototype selection and the training set selection for data reduction in KDD. This paper includes a comparison between these algorithms and other nonevolutionary instance selection algorithms. The results show that the evolutionary instance selection algorithms consistently outperform the nonevolutionary ones, the main advantages being: better instance reduction rates, higher classification accuracy, and models that are easier to interpret.