Title
Empirical comparison of resampling methods using genetic fuzzy systems for a regression problem
Abstract
Much attention has been given in machine learning field to the study of numerous resampling techniques during the last fifteen years. In the paper the investigation of m-out-of-n bagging with and without replacement and repeated cross-validation using genetic fuzzy systems is presented. All experiments were conducted with real-world data derived from a cadastral system and registry of real estate transactions. The bagging ensembles created using genetic fuzzy systems revealed prediction accuracy not worse than the experts' method employed in reality. It confirms that automated valuation models can be successfully utilized to support appraisers' work.
Year
DOI
Venue
2011
10.1007/978-3-642-23878-9_3
IDEAL
Keywords
Field
DocType
prediction accuracy,automated valuation model,bagging ensemble,resampling method,real-world data,empirical comparison,cadastral system,regression problem,genetic fuzzy system,numerous resampling technique,real estate transaction,last fifteen year,m-out-of-n bagging,bagging,cross validation
Empirical comparison,Data mining,Real estate,Cadastre,Computer science,Artificial intelligence,Regression problems,Resampling,Cross-validation,Valuation (finance),Machine learning,Genetic fuzzy systems
Conference
Volume
ISSN
Citations 
6936
0302-9743
14
PageRank 
References 
Authors
0.59
18
4
Name
Order
Citations
PageRank
Tadeusz Lasota134825.33
Zbigniew Telec217014.92
Grzegorz Trawiński3474.81
Bogdan Trawiński428824.72