Optimal Feature Selection for Decision Robustness in Bayesian Networks
Optimal Feature Selection for Decision Robustness in Bayesian Networks
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DOI：
10.24963/ijcai.2017/215
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发表时间：
2017-08
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影响因子：
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通讯作者：
YooJung Choi;Adnan Darwiche;Guy Van den Broeck
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文献类型：
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作者：
YooJung Choi;Adnan Darwiche;Guy Van den Broeck
在许多应用程序中，可以定义一大批功能以支持手头的分类任务。然而，在测试时，评估的昂贵昂贵，并且仅使用一小部分功能，通常是为了其信息理论价值。对于基于阈值的Naive Bayes分类器，最近的工作建议选择功能，以最大程度地提高分类器的预期鲁棒性，即，看到更多功能后，其预期的概率。我们提出了第一种基于将网络编译为可拖动的电路表示的算法来计算总贝叶斯网络分类器的预期相同否定概率。此外，我们为最佳特征选择开发了一种搜索算法，该算法利用有效的增量电路修改。关于天真的贝叶斯以及更通用的网络的实验显示了这种决策方法的效力和独特行为。
In many applications, one can define a large set of features to support the classification task at hand. At test time, however, these become prohibitively expensive to evaluate, and only a small subset of features is used, often selected for their information-theoretic value. For threshold-based, Naive Bayes classifiers, recent work has suggested selecting features that maximize the expected robustness of the classifier, that is, the expected probability it maintains its decision after seeing more features. We propose the first algorithm to compute this expected same-decision probability for general Bayesian network classifiers, based on compiling the network into a tractable circuit representation. Moreover, we develop a search algorithm for optimal feature selection that utilizes efficient incremental circuit modifications. Experiments on Naive Bayes, as well as more general networks, show the efficacy and distinct behavior of this decision-making approach.