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Assessment for Different Neural Networks with FeatureSelection in Classification Issue.

Joy Iong-Zong Chen1, Chung-Sheng Pi1

  • 1Department of Electrical Engineering, Da-Yeh University, Chunghua 515006, Taiwan.

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|April 23, 2022
PubMed
Summary

This study introduces a novel correlation coefficient (CC) assignment scheme with feature selection (FS) to enhance neural network (NN) computing. The method improves accuracy by reducing strict FS, with GoogLeNet showing the best performance.

Keywords:
CC (correlation coefficient)FS (feature selection)NN (neural network)self-revision learningsupervised learning

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Neural network (NN) computing involves complex algorithms like parallel computing and data optimization.
  • Supervised learning in NNs relies on parameters such as self-revised learning and input datasets.
  • Adjusting NN connection synapses' weights enables self-learning computer systems.

Purpose of the Study:

  • To develop an adaptive correlation coefficient (CC) assignment scheme integrated with feature selection (FS) categories.
  • To address limitations in NN computing, including high-dimensional data, data overfitting, and strict FS problems.

Main Methods:

  • The proposed CC assignment scheme was implemented using the Fruits-360 dataset, comprising 20,860 images across 120 fruit varieties.
  • Three NN frameworks—AlexNet, GoogLeNet, and ResNet101—were utilized to examine NN system accuracy, performance, and loss rate.
  • The scheme was evaluated by comparing accuracy rates achieved with reduced strict FS.

Main Results:

  • The proposed CC assignment scheme, when combined with FS, demonstrated an improved accuracy rate in recognition tasks.
  • The GoogLeNet model exhibited the most significant feature selection performance among the tested NN frameworks.
  • Reducing strict feature selection was found to enhance the overall accuracy rate.

Conclusions:

  • The developed CC assignment schemes are valuable for designing and selecting NN training models for effective feature discrimination.
  • The FS-based CC assignment approach offers superior performance compared to existing state-of-the-art methods.
  • This research validates the effectiveness of adaptive CC assignment and FS in optimizing NN performance.