Machine Learning
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
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ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
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The experiments aimed to compare the performance of random subspace and random forest models with bagging ensembles and single models in respect of its predictive accuracy were conducted using two popular algorithms M5 tree and multilayer perceptron. All tests were carried out in the WEKA data mining system within the framework of 10-fold cross-validation and repeated holdout splits. A comprehensive real-world cadastral dataset including over 5200 samples and recorded during 11 years served as basis for benchmarking the methods. The overall results of our investigation were as follows. The random forest turned out to be superior to other tested methods, the bagging approach outperformed the random subspace method, single models provided worse prediction accuracy than any other ensemble technique.