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Cost-sensitive multi-kernel ELM based on reduced expectation kernel auto-encoder.

Liang Yixuan1,2

  • 1School of Science, Xi 'an University of Technology, Xi'an, Shaanxi, P. R. China.

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Summary
This summary is machine-generated.

This study introduces a novel multi-kernel cost-sensitive Extreme Learning Machine (ELM) method using an expectation kernel auto-encoder. This approach aims to improve training speed and generalization performance for kernel-based ELM models.

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computational Science

Background:

  • Extreme Learning Machines (ELM) offer rapid training and strong generalization.
  • Existing kernel ELM auto-encoders suffer from long training times and complex parameter tuning.
  • Multi-kernel models face challenges in setting kernel function weights.

Purpose of the Study:

  • To propose a novel multi-kernel cost-sensitive ELM method.
  • To address the limitations of existing kernel ELM auto-encoders and multi-kernel approaches.
  • To enhance training efficiency and generalization performance in ELM models.

Main Methods:

  • A reduced kernel auto-encoder is defined using random reference points for similarity.
  • A reduced expectation kernel auto-encoder is designed, combining random and similarity mapping.
  • Two multi-kernel ELM models are developed, converting classifier output to posterior probability.
  • Cost-sensitive decision-making is implemented via the minimum risk criterion.

Main Results:

  • The proposed method effectively addresses long training times and parameter setting difficulties.
  • Experimental validation on public and realistic datasets demonstrates the method's efficacy.
  • The approach successfully integrates expectation kernel ELM with multi-kernel strategies.

Conclusions:

  • The developed multi-kernel cost-sensitive ELM method offers an effective solution for kernel-based ELM.
  • The method shows significant improvements in training efficiency and generalization.
  • This work contributes to advancing kernel ELM auto-encoder methodologies.