Abstract
A modified version of Boosted Mixture of Experts (BME) is presented in this paper. While previous related works, namely BME, attempt to improve the performance by incorporating complementary features of a hybrid combining framework, they have some drawback. Analyzing the problems of previous approaches has suggested several modifications that have led us to propose a new method called Boost-wise Pre-loaded Mixture of Experts (BPME). We present a modification in pre-loading (initialization) procedure of ME, which addresses previous problems and overcomes them by employing a two-stage pre-loading procedure. In this approach, both the error and confidence measures are used as the difficulty criteria in boost-wise partitioning of problem space.
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Ebrahimpour, R., Sadeghnejad, N., Arani, S.A.A.A. et al. Boost-wise pre-loaded mixture of experts for classification tasks. Neural Comput & Applic 22 (Suppl 1), 365–377 (2013). https://doi.org/10.1007/s00521-012-0909-2
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DOI: https://doi.org/10.1007/s00521-012-0909-2