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An Ensemble of Bayesian Networks for Multilabel Classification

机译：贝叶斯网络的多标签分类

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摘要

We present a novel approach for multilabel classification based on an ensemble of Bayesian networks.The class variables are connected by a tree;each model of the ensemble uses a different class as root of the tree.We assume the features to be conditionally independent given the classes,thus generalizing the naive Bayes assumption to the multiclass case.This assumption allows us to optimally identify the correlations between classes and features;such correlations are moreover shared across all models of the ensemble.Inferences are drawn from the ensemble via logarithmic opinion pooling.To minimize Hamming loss,we compute the marginal probability of the classes by running standard inference on each Bayesian network in the ensemble,and then pooling the inferences.To instead minimize the subset 0/1 loss,we pool the joint distributions of each model and cast the problem as a MAP inference in the corresponding graphical model.Experiments show that the approach is competitive with state-of-the-art methods for multilabel classification.
机译：我们提出了一种基于贝叶斯网络集合的多标签分类的新方法，类变量由树连接;集合的每个模型都使用不同的类作为树的根。假设给定的特征是条件独立的类，因此将朴素贝叶斯假设推广到多类情况。此假设使我们能够最佳地识别类和要素之间的相关性；而且这些相关性在整个集成模型中都是共享的。通过对数意见汇总从集成中得出推论。为了最大程度地减少汉明损失，我们通过在集合中的每个贝叶斯网络上运行标准推理来计算类的边际概率，然后汇总推理。为使子集0/1损失最小化，我们汇总了每个模型的联合分布实验将问题作为MAP推论推导到相应的图形模型中。实验表明，该方法具有竞争优势用于多标签分类的电子方法。

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