Title
Comparison Of Ensemble Approaches: Mixture Of Experts And Adaboost For A Regression Problem
Abstract
Two machine learning approaches: mixture of experts and AdaBoost.R2 were adjusted to the real-world regression problem of predicting the prices of residential premises based on historical data of sales/purchase transactions. The computationally intensive experiments were conducted aimed to compare empirically the prediction accuracy of ensemble models generated by the methods. The analysis of the results was performed using statistical methodology including nonparametric tests followed by post-hoc procedures designed especially for multiple nxn comparisons. No statistically significant differences were observed among the best ensembles: two generated by mixture of experts and two by AdaBoost.R2 employing multilayer perceptrons and general linear models as base learning algorithms.
Year
DOI
Venue
2014
10.1007/978-3-319-05458-2_11
INTELLIGENT INFORMATION AND DATABASE SYSTEMS, PT II
Keywords
Field
DocType
mixture of experts, AdaBoost.R2, mlp, glm, svr, real estate appraisal, Matlab
Data mining,AdaBoost,MATLAB,Ensemble forecasting,Computer science,Nonparametric statistics,Generalized linear model,Mixture of experts,Artificial intelligence,Perceptron,Ensemble learning,Machine learning
Conference
Volume
ISSN
Citations 
8398
0302-9743
2
PageRank 
References 
Authors
0.49
31
4
Name
Order
Citations
PageRank
Tadeusz Lasota134825.33
Bartosz Londzin240.86
Zbigniew Telec317014.92
Bogdan Trawinski411512.89