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Structured features in naive bayes classification

Choi, A ; Sharif University of Technology | 2016

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  1. Type of Document: Article
  2. Publisher: AAAI press , 2016
  3. Abstract:
  4. 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. © Copyright 2016, Association for the Advancement of Artificial Intelligence
  5. Keywords:
  6. Artificial intelligence ; Classifiers ; Graph theory ; Probability distributions ; Anomalous path ; Case-studies ; Combinatorial objects ; Decision diagram ; Naive Bayes classification ; Naive Bayes classifiers ; Simple games ; Skill levels ; Classification (of information)
  7. Source: 30th AAAI Conference on Artificial Intelligence, AAAI 2016, 12 February 2016 through 17 February 2016 ; 2016 , Pages 3233-3240 ; 9781577357605 (ISBN)