Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets
Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets
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DOI：
10.1016/s0031-3203(02)00121-8
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发表时间：
2003-06-01
影响因子：
8
通讯作者：
Quek, F
中科院分区：
文献类型：
--
作者：
Bryll, R;Gutierrez-Osuna, R;Quek, F
我们提出了属性bagging (AB)，这是一种利用随机特征子集来提高分类器集成的准确性和稳定性的技术。AB是一种包装方法，可以与任何学习算法一起使用。它建立一个适当的属性子集大小，然后随机选择特征子集，创建训练集的投影，在其上构建集成分类器。然后使用诱导分类器进行投票。本文将我们的AB方法与bagging和其他算法在手部姿势识别数据集上的性能进行了比较。结果表明，AB在准确性和稳定性方面都优于装袋。测试和讨论了套袋和AB方法中集成投票的性能作为属性子集大小和加权和非加权投票的投票人数量的函数。我们还证明，根据分类精度对属性子集进行排序，并仅使用最佳子集进行投票，进一步提高了集成的最终性能。(C) 2002模式识别学会。Elsevier Science Ltd.出版。版权所有。
We present attribute bagging (AB), a technique for improving the accuracy and stability of classifier ensembles induced using random subsets of features. AB is a wrapper method that can be used with any learning algorithm. It establishes an appropriate attribute subset size and then randomly selects subsets of features, creating projections of the training set on which the ensemble classifiers are built. The induced classifiers are then used for voting. This article compares the performance of our AB method with bagging and other algorithms on a hand-pose recognition dataset. It is shown that AB gives consistently better results than bagging, both in accuracy and stability. The performance of ensemble voting in bagging and the AB method as a function of the attribute subset size and the number of voters for both weighted and unweighted voting is tested and discussed. We also demonstrate that ranking the attribute subsets by their classification accuracy and voting using only the best subsets further improves the resulting performance of the ensemble. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.