Learning in a Fuzzy Random Forest Ensemble from Imperfect Data

被引：0
|
作者：
Cadenas, Jose M. [1 ]
Carmen Garrido, M. [1 ]
Martinez, Raquel [1 ]
机构：
[1] Univ Murcia, Fac Informat, Dpt Engn Informat & Commun, Murcia, Spain
来源：
2011 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | 2011年
关键词：
Fuzzy Sets; Imperfect data; Classification Technique;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Instrument errors or noise interference during experiments may lead to incomplete data when measuring a specific attribute. Obtaining models from imperfect data is a topic currently being treated with more interest. In this paper, we present the learning phase of a Fuzzy Random Forest ensemble for classification from imperfect data. We perform experiments with imperfect datasets created for this purpose and datasets used in other papers to show the express the true nature of imperfect information.
引用
收藏
页码：277 / 282
页数：6
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