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Abstract
Naive Bayes is one of the most efficient and effective inductive learning algorithms for machine learning and data mining. Its competitive performance in classifica-tion is surprising, because the conditional independence assumption on which it is based, is rarely true in real-world applications. An open question is: what is the true reason for the surprisingly good performance of naive Bayes in classification? In this paper, we propose a novel explanation on the superb classification performance of naive Bayes. We show that, essentially, the dependence distribution; i.e., how the local dependence of a node distributes in each class, evenly or unevenly, and how the local dependen-cies of all nodes work together, consistently (support-
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The record
- Venue
- Topic
- Bayesian Modeling and Causal Inference
- Field
- Computer Science
- Canadian institutions
- University of New Brunswick
- Funders
- —
- Keywords
- Naive Bayes classifierMachine learningBayesian programmingBayes error rateArtificial intelligenceConditional independenceBayes classifierBayes' theoremComputer scienceClassifier (UML)MathematicsBayes factorSupport vector machineBayesian probability
- Has abstract in OpenAlex
- yes