Evaluation of Random Subspace and Random Forest Regression Models Based on Genetic Fuzzy Systems
Tadeusz Lasota, Zbigniew Telec, Trawi nacute ski Bogdan, Trawi nacute ski Grzegorz · Frontiers in artificial intelligence and applications · 2012
The random subspace and random forest ensemble methods using a genetic fuzzy rule-based system as a base learning algorithm 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 computationally intensive experiments were conducted aimed to compare the accuracy of ensembles generated by the proposed methods with bagging, repeated holdout, and repeated cross-validation models. The statistical analysis of results was made employing nonparametric Friedman and Wilcoxon statistical tests.