Investigation of random subspace and random forest regression models using data with injected noise

Authors:
Tadeusz Lasota;Zbigniew Telec;Bogdan Trawiński;Grzegorz Trawiński
Affiliations:
Dept. of Spatial Management, Wrocław University of Environmental and Life Sciences, Wrocław, Poland;Institute of Informatics, Wrocław University of Technology, Wrocław, Poland;Institute of Informatics, Wrocław University of Technology, Wrocław, Poland;Faculty of Electronics, Wrocław University of Technology, Wrocław, Poland
Venue:
KES'12 Proceedings of the 16th international conference on Knowledge Engineering, Machine Learning and Lattice Computing with Applications
Year:
2012

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Abstract

The ensemble machine learning methods incorporating random subspace and random forest employing genetic fuzzy rule-based systems as base learning algorithms were developed in Matlab environment. The methods were applied to the real-world regression problem of predicting the prices of residential premises based on historical data of sales/purchase transactions. The accuracy of ensembles generated by the proposed methods was compared with bagging, repeated holdout, and repeated cross-validation models. The tests were made for four levels of noise injected into the benchmark datasets. The analysis of the results was performed using statistical methodology including nonparametric tests followed by post-hoc procedures designed especially for multiple N×N comparisons.