Empirical comparison of bagging ensembles created using weak learners for a regression problem
Karol Bańczyk , Olgierd Kempa , Tadeusz Lasota , Bogdan Trawiński
AbstractThe experiments, aimed to compare the performance of bagging ensembles using three different test sets composed of base, out-of-bag, and 30% holdout instances were conducted. Six weak learners including conjunctive rules, decision stump, decision table, pruned model trees, rule model trees, and multilayer perceptron, implemented in the data mining system WEKA, were applied. All algorithms were employed to real-world datasets derived from the cadastral system and the registry of real estate transactions, and cleansed by property valuation experts. The analysis of the results was performed using recently proposed statistical methodology including nonparametric tests followed by post-hoc procedures designed especially for multiple n×n comparisons. The results showed the lowest prediction error with base test set only in the case of model trees and a neural network.
|Journal series||Lecture Notes In Computer Science, ISSN 0302-9743, [1611-3349], (A 13 pkt)|
|Publication size in sheets||0.5|
|Conference||3rd International Conference on Intelligent Information and Database Systems (ACIIDS 2011), 20-04-2011 - 22-04-2011, Daegu, Korea Południowa|
|Keywords in English||Bagging; Ensemble models; Out-of-bag; Property valuation; WEKA|
|Publication indicators||= 5; = 6|
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