G-REX: A Versatile Framework for Evolutionary Data Mining

Rikard König, U. Johansson, L. Niklasson
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引用次数: 30

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.
G-REX:进化数据挖掘的通用框架
提出了一种基于遗传规划的通用数据挖掘框架G-REX。G-REX与其他GP框架的不同之处在于,它并不力求成为一个通用框架。这允许G-REX包含更多特定于数据挖掘的功能,如预处理、评估和优化方法,以及大量预定义的分类和回归模型。预定义模型的例子有决策树、决策列表、带有属性权重的k-NN、混合knn规则、模糊规则和几种不同的回归模型。然而,它的主要优点是灵活性,可以很容易地修改、扩展和组合所有预定义的功能。此外,G-REX还包含在一个特殊的Weka包中,为标准数据挖掘工具Weka添加了有用的进化功能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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