{"title":"G-REX: A Versatile Framework for Evolutionary Data Mining","authors":"Rikard König, U. Johansson, L. Niklasson","doi":"10.1109/ICDMW.2008.117","DOIUrl":null,"url":null,"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.","PeriodicalId":175955,"journal":{"name":"2008 IEEE International Conference on Data Mining Workshops","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"30","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE International Conference on Data Mining Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMW.2008.117","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.