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Boost-wise pre-loaded mixture of experts for classification tasks

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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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Authors and Affiliations

  1. Brain and Intelligent Systems Research Laboratory, Department of Electrical and Computer Engineering, Shahid Rajaee Teacher Training University, P.O. Box 16785-163, Tehran, Iran

    Reza Ebrahimpour, Naser Sadeghnejad, Seyed Ali Asghar Abbaszadeh Arani & Nima Mohammadi

  2. School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), P.O. Box 19395-5746, Tehran, Iran

    Reza Ebrahimpour

Authors
  1. Reza Ebrahimpour
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  2. Naser Sadeghnejad
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  3. Seyed Ali Asghar Abbaszadeh Arani
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  4. Nima Mohammadi
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Correspondence to Reza Ebrahimpour.

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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

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