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NonIncrementalClassificationLearner

classifier = AGGREGATION({AGGREGATIONSCLASSIFICATION_LEARNER(
	{LABELATTRIBUTE = 'label', 
     ALGORITHM = 'WekaGeneric', 
     SUBALGORITHM = 'J48', 
     CLASSIFIEROPTIONS = '-U'}, windowed)

Remark: Internally, this will be translated to. See Explanations of parameters there (LABELATTRIBUTE = LABEL_ATTRIBUTE, SUBALGORITHM=WEKA_ALGORITHM, CLASSIFIEROPTIONS=WEKA_OPTIONS)

classifier = AGGREGATION({
                  aggregations = [
	                    ['FUNCTION' = 'NonIncrementalClassificationLearner', 
				     'LABEL_ATTRIBUTE' = 'label', 	
                     'ALGORITHM' = 'WekaGeneric', 
                     'WEKA_ALGORITHM' = 'J48',
	 
                     'WEKA_OPTIONS' = '-U']
	], EVAL_AT_NEW_ELEMENT                  ],
                  eval_at_new_element = false, EVAL_BEFORE_REMOVE_OUTDATING
                  eval_before_remove_outdating = true
                },
                windowed
              )

This first version, the NonIncrementalClassificationLearner, is a wrapper for WEKA classifier (current supported version is 3.8) learners and needs the following parameters

• LABEL_ATTRIBUTE: In the input data, which is the attribute with the label, that should be learned
• WEKA_ALGORITHM: Which WEKA Algorithm should be used. At the moment, the following algorithms are available. See WEKA for more information:
  • BayesNet
  • NaiveBayes
  • NaiveBayesMultinomial
  • NaiveBayesUpdateable
  • GaussianProcesses
  • Logistic
  • MultilayerPerceptron
  • SimpleLogistic
  • SMO
  • IBk
  • KStar
  • LWL
  • DecisionTable
  • JRip
  • OneR
  • PART
  • DecisionStump
  • HoeffdingTree
  • J48
  • LMT
  • RandomForest
  • RandomTree
  • REPTree
• WEKA_OPTIONS: The options that should be given for the algorithm (see https://weka.sourceforge.io/doc.stable/weka/classifiers/Classifier.html for information about the given parameters)

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