Publication | Closed Access
G-REX: A Versatile Framework for Evolutionary Data Mining
40
Citations
4
References
2008
Year
Unknown Venue
EngineeringMachine LearningIntelligent SystemsOptimization-based Data MiningPhylogeneticsData ScienceData MiningPattern RecognitionDecision Tree LearningEvolution-based MethodPredictive AnalyticsKnowledge DiscoveryComputer SciencePopulation GeneticsVersatile FrameworkBiologyOther Gp FrameworksEvolutionary Data MiningGenetic AlgorithmsNatural SciencesRule InductionEvolutionary BiologyClassificationDecision TreesLearning Classifier System
This paper presents G-REX, a versatile data mining framework based on genetic programming. What differs G-REX from other GP frameworks is that it doesn't strive to be a general purpose framework. This allows G-REX to include more functionality specific to data mining like preprocessing, evaluation- and optimization methods, but also a multitude of predefined classification and regression models. Examples of predefined models are decision trees, decision lists, k-NN with attribute weights, hybrid kNN-rules, fuzzy-rules and several different regression models. The main strength is, however, the flexibility, making it easy to modify, extend and combine all of the predefined functionality. G-REX is, in addition, available in a special Weka package adding useful evolutionary functionality to the standard data mining tool Weka.
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