Empirical Comparison of Resampling Methods Using Genetic Fuzzy Systems for a Regression Problem

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 experime...

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Bibliographic Details
Published inIntelligent Data Engineering and Automated Learning - IDEA 2011 Vol. 6936; pp. 17 - 24
Main Authors Lasota, Tadeusz, Telec, Zbigniew, Trawiński, Grzegorz, Trawiński, Bogdan
Format Book Chapter
LanguageEnglish
Published Germany Springer Berlin / Heidelberg 2011
Springer Berlin Heidelberg
SeriesLecture Notes in Computer Science
Subjects
Artificial intelligence
bagging
cross-validation
genetic fuzzy systems
Information retrieval
subagging
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Summary: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.
ISBN:9783642238772
3642238777
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-642-23878-9_3