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MapReduce-based big data classification model using feature subset selection and hyperparameter tuned deep belief

Surendran Rajendran1, Osamah Ibrahim Khalaf2, Youseef Alotaibi3

  • 1Center for Artificial Intelligence and Research (CAIR), Chennai Institute of Technology, Chennai, India. surendran.phd.it@gmail.com.

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|December 18, 2021
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Summary
This summary is machine-generated.

This study introduces a novel big data classification model using chaotic pigeon inspired optimization (CPIO) for feature selection and Harris hawks optimization (HHO) for deep belief network (DBN) tuning, enhancing classification accuracy.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Big data classification is crucial across various sectors like healthcare and finance.
  • Feature selection enhances big data classification efficiency and accuracy.
  • Metaheuristic optimization algorithms are effective for feature selection.

Purpose of the Study:

  • To design an optimized big data classification model.
  • To integrate chaotic pigeon inspired optimization (CPIO) for feature selection.
  • To utilize Harris hawks optimization (HHO) for tuning deep belief network (DBN) hyperparameters.

Main Methods:

  • A big data classification model was developed using the Hadoop MapReduce environment.
  • The CPIO algorithm was employed for selecting optimal feature subsets.
  • The HHO algorithm was used to optimize the DBN classifier's hyperparameters for improved performance.

Main Results:

  • The proposed model demonstrated superior performance in big data classification.
  • Simulations across various dimensions confirmed the technique's effectiveness.
  • The CPIO-based feature selection and HHO-tuned DBN significantly boosted classification accuracy.

Conclusions:

  • The developed model offers an effective approach for big data classification.
  • The integration of CPIO and HHO optimizes feature selection and classifier performance.
  • This technique shows significant advantages over existing methods for big data analysis.