Evolving Fuzzy Systems Based on the eTS Learning Algorithm for the Valuation of Residential Premises

被引：0

作者：

Lasota, Tadeusz ^{[1
]}

Telec, Zbigniew ^{[2
]}

Trawinski, Bogdan ^{[2
]}

Trawinski, Krzysztof ^{[3
]}

机构：

[1] Wroclaw Univ Environm & Life Sci, Dept Spatial Management, Ul Norwida 25-27, PL-50375 Wroclaw, Poland

[2] Wroclaw Univ Technol, Inst Informat, PL-50370 Wroclaw, Poland

[3] Edificio Cient Tecnol, European Ctr Soft Computing, Asturias, Spain

来源：

INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING, PROCEEDINGS | 2009年 / 5788卷

关键词：

evolving fuzzy systems; eTS learning algorithm; property valuation; feature selection; GIS;

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：

An attempt has been made to employ evolving Takagi-Sugeno algorithm (eTS) to built models assisting property valuation on the basis of actual data drawn from cadastral system, registry of sales transactions, and a cadastral map. Seven methods of feature selection were applied an evaluated. The eTS performance was compared to three algorithms implemented in KEEL, including decision trees for regression. neural network. and Support vector machine. The results confirmed the advantages of the eTS algorithm.

引用

页码：594 / +

页数：2

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