Learning Classifier Systems
Learning Classifier Systems
复制标题
学习分类器系统
DOI：
10.1007/978-3-540-88138-4_15
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
Bacardit J
中科院分区：
文献类型：
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作者：
Bacardit J
集成技术已被证明在提高几种类型的机器学习方法的性能方面非常成功。在本文中，我们结合匹兹堡风格的学习分类器系统GAssists来说明它的有效性。测试了两种类型的合奏。首先，我们评估了一个用于共识预测的集合。在这种情况下，使用GAsset学习的具有不同初始随机种子的几个规则集使用平面投票方案以类似于打包的方式被组合。第二种类型的集合旨在更有效地处理有序分类问题。也就是说，类之间具有某种内在顺序的问题，并且在错误分类的情况下，最好预测与类内在顺序中的正确类接近的类。共识预测的集合使用来自UCI储存库的25个数据集进行评估。使用生物信息学数据集来评估该分层集成。这两种方法都显著提高了GAssists在所有测试域中的性能和行为。
Ensemble techniques have proved to be very successful in boosting the performance of several types of machine learning methods. In this paper, we illustrate its usefulness in combination with GAssist, a Pittsburgh-style Learning Classifier System. Two types of ensembles are tested. First we evaluate an ensemble for consensus prediction. In this case several rule sets learnt using GAssist with different initial random seeds are combined using a flat voting scheme in a fashion similar to bagging. The second type of ensemble is intended to deal more efficiently with ordinal classification problems. That is, problems where the classes have some intrinsic order between them and, in case of misclassification, it is preferred to predict a class that is close to the correct one within the class intrinsic order. The ensemble for consensus prediction is evaluated using 25 datasets from the UCI repository. The hierarchical ensemble is evaluated using a Bioinformatics dataset. Both methods significantly improve the performance and behaviour of GAssist in all the tested domains.