Comparison of ensemble approaches: Mixture of experts and Ada Boost for a regression problem

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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 n×n 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. © 2014 Springer International Publishing Switzerland.

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Lasota, T., Londzin, B., Telec, Z., & Trawiński, B. (2014). Comparison of ensemble approaches: Mixture of experts and Ada Boost for a regression problem. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8398 LNAI, pp. 100–109). Springer Verlag. https://doi.org/10.1007/978-3-319-05458-2_11

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