averagedOneDependenceEstimators · AODE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes. The resulting algorithm is computationally efficient while delivering highly accurate classification on many learning tasks. For more information, see G. Webb, J. Boughton, Z. Wang (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning. 58(1):5-24.

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Version		Vulnerabilities	Size	Updated
1.2.x	averagedOneDependenceEstimators-1.2.1.jar		22.27 KB	Jul 20, 2012
1.0.x	averagedOneDependenceEstimators-1.0.2.jar		22.27 KB	Apr 26, 2012
	averagedOneDependenceEstimators-1.0.1.jar		22.27 KB	Apr 24, 2012

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weka.classifiers.bayes

├─ weka.classifiers.bayes.AODE.class - [JAR]

├─ weka.classifiers.bayes.AODEsr.class - [JAR]

├─ weka.classifiers.bayes.WAODE.class - [JAR]