Author :
Konig, Rikard ; Johansson, Ulf ; Niklasson, Lars
Abstract :
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.
Keywords :
data mining; decision trees; fuzzy set theory; genetic algorithms; regression analysis; G-REX; Weka; decision lists; decision trees; evolutionary data mining; fuzzy-rules; genetic programming; hybrid kNN-rules; regression models; Conferences; Data mining; Decision trees; Genetic programming; Graphical user interfaces; Java; Measurement; Optimization methods; Packaging; Regression tree analysis; Data Mining; Framework; Genetic Programming; Representation;