Abstract
In this paper a novel hybrid ensemble method aiming at the improvement of models accuracy in regression tasks is presented. The proposed ensemble is composed by a strong learner trained exploiting data belonging to the whole training dataset and a set of specialised weak learners trained by using data coming from limited regions of the input space determined by means of a Self Organising Map based clustering. In the simulation phase, the strong and weak learners operate alternatively according to their punctual self-estimated reliabilities so as to handle each specific sample by means of the most promising learner. The method has been tested both on literature and real world datasets achieving satisfactory results.
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Vannucci, M., Colla, V., Cateni, S. (2015). An Hybrid Ensemble Method Based on Data Clustering and Weak Learners Reliabilities Estimated Through Neural Networks. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9095. Springer, Cham. https://doi.org/10.1007/978-3-319-19222-2_34
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DOI: https://doi.org/10.1007/978-3-319-19222-2_34
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