Title
Investigation of incremental support vector regression applied to real estate appraisal
Abstract
Incremental support vector regression algorithms (SVR) and sequential minimal optimization algorithms (SMO) for regression were implemented. Intensive experiments to compare predictive accuracy of the algorithms with different kernel functions over several datasets taken from a cadastral system were conducted in offline scenario. The statistical analysis of experimental output was made employing the nonparametric methodology designed especially for multiple N×N comparisons of N algorithms over N datasets including Friedman tests followed by Nemenyi's, Holm's, Shaffer's, and Bergmann-Hommel's post-hoc procedures. The results of experiments showed that most of SVR algorithms outperformed significantly a pairwise comparison method used by the experts to estimate the values of residential premises over all datasets. Moreover, no statistically significant differences between incremental SVR and non-incremental SMO algorithms were observed using our stationary cadastral datasets. The results open the opportunity of further research into the application of incremental SVR algorithms to predict from a data stream of real estate sales/purchase transactions.
Year
DOI
Venue
2013
10.1007/978-3-642-36543-0_20
ACIIDS
Keywords
Field
DocType
svr algorithm,stationary cadastral datasets,real estate appraisal,incremental svr,non-incremental smo algorithm,multiple n,incremental svr algorithm,cadastral system,n algorithm,incremental support vector regression,n comparison,support vector regression
Pairwise comparison,Data mining,Real estate appraisal,Regression,Data stream,Computer science,Support vector machine,Nonparametric statistics,Artificial intelligence,Sequential minimal optimization,Machine learning,Kernel (statistics)
Conference
Volume
ISSN
Citations 
7803
0302-9743
0
PageRank 
References 
Authors
0.34
15
4
Name
Order
Citations
PageRank
Tadeusz Lasota134825.33
Petru Patrascu200.34
Bogdan Trawiński328824.72
Zbigniew Telec417014.92