Structured Features in Naive Bayes Classification
Structured Features in Naive Bayes Classification
复制标题
朴素贝叶斯分类中的结构化特征
DOI：
10.1609/aaai.v30i1.10427
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Adnan Darwiche
中科院分区：
文献类型：
--
作者：
Arthur Choi;N. Tavabi;Adnan Darwiche
我们提出了结构化天真贝叶斯（SNB）分类器，它利用结构化特征增强了无处不在的天真贝叶斯分类器。SNB 分类器便于以通用但系统的方式使用复杂特征，如组合对象（如图、路径和顺序）。SNB 分类器的基础是最近提出的概率句式判定图 (PSDD)，它是结构化空间上概率分布的一种简便表示法。我们通过案例研究来说明 SNB 分类器的实用性和通用性。首先，我们展示了如何纯粹通过观察简单游戏中玩家的游戏风格和技术水平来区分他们。其次，我们展示了如何纯粹通过观察路径本身来检测图上的异常路径。
We propose the structured naive Bayes (SNB) classifier, which augments the ubiquitous naive Bayes classifier with structured features. SNB classifiers facilitate the use of complex features, such as combinatorial objects (e.g., graphs, paths and orders) in a general but systematic way. Underlying the SNB classifier is the recently proposed Probabilistic Sentential Decision Diagram (PSDD), which is a tractable representation of probability distributions over structured spaces. We illustrate the utility and generality of the SNB classifier via case studies. First, we show how we can distinguish players of simple games in terms of play style and skill level based purely on observing the games they play. Second, we show how we can detect anomalous paths taken on graphs based purely on observing the paths themselves.