Bagging has attracted much attention due to its simple implementation and the popularity of bootstrapping. By learning diverse classifiers from resampled datasets and averaging the outcomes, bagging investigates the possibility of achieving substantial classification performance of the base classifier. Diversity has been recognized as a very important characteristic in bagging. This paper presents an efficient and effective bagging approach, that learns a set of independent Bayesian network classifiers (BNCs) from disjoint data subspaces. The number of bits needed to describe the data is measured in terms of log likelihood, and redundant edges are identified to optimize the topologies of the learned BNCs. Our extensive experimental evaluation on 54 publicly available datasets from the UCI machine learning repository reveals that the proposed algorithm achieves a competitive classification performance compared with state-of-the-art BNCs that use or do not use bagging procedures, such as tree-augmented naive Bayes (TAN), k-dependence Bayesian classifier (KDB), bagging NB or bagging TAN.

中文翻译：

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套袋k相关贝叶斯网络分类器

套袋由于其简单的实现和自举的流行而备受关注。通过从重采样的数据集中学习不同的分类器并平均结果，bagging研究了实现基本分类器实质性分类性能的可能性。在装袋中，多样性已被认为是非常重要的特征。本文提出了一种有效且有效的装袋方法，该方法从不相交的数据子空间中学习了一组独立的贝叶斯网络分类器（BNC）。描述数据所需的位数以对数似然性进行衡量，并标识冗余边以优化学习的BNC的拓扑。