MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.
-AMRules now uses weights for examples in all its extent.
-New: RandomAMRules ensemble method. Uses bagging to perturb training set (using AMRules support for weights), and at each rule expansion a different subset of the attributes is used.
-Fixed minor RandomRules bugs.
Main difference between RandomRules and RandomAMRules: each model of the former sees a different subset of the attributes while the latter uses different subset of the attributes for expanding each rule.
-AMRules now uses weights for examples in all its extent. -New: RandomAMRules ensemble method. Uses bagging to perturb training set (using AMRules support for weights), and at each rule expansion a different subset of the attributes is used. -Fixed minor RandomRules bugs.
Main difference between RandomRules and RandomAMRules: each model of the former sees a different subset of the attributes while the latter uses different subset of the attributes for expanding each rule.