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RST-DE: Rough Sets-Based New Differential Evolution Algorithm for Scalable Big Data Feature Selection in Distributed

Santosh Thakur1, Ramesh Dharavath2, Achyut Shankar3

  • 1School of Technology, Woxsen University, Hyderabad, India.

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|May 5, 2022
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Summary
This summary is machine-generated.

This study introduces a new feature selection framework using rough sets and differential evolution (DE) on Hadoop and Apache Spark. The novel approach enhances data analysis accuracy and scalability by efficiently identifying and removing irrelevant features.

Keywords:
HadoopSparkbig datafeature selectionrough sets

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Area of Science:

  • Computer Science
  • Data Science
  • Machine Learning

Background:

  • Exponential data growth and increasing features lead to big data dimensionality challenges.
  • High feature volume often contains redundant data, negatively impacting classification accuracy.
  • Effective feature selection is crucial for improving scalability and accuracy in data analysis.

Purpose of the Study:

  • To present a novel feature selection framework addressing big data dimensionality.
  • To improve the scalability and accuracy of feature classification in large datasets.
  • To integrate rough sets and differential evolution (DE) for optimal feature identification.

Main Methods:

  • Implementation of a new feature selection framework on Hadoop and Apache Spark platforms.
  • Utilizing rough sets to identify minimal feature subsets.
  • Employing the differential evolution (DE) algorithm to optimize feature selection by considering data overlap.

Main Results:

  • The proposed framework was evaluated using Random Forest and Naive Bayes classifiers on five benchmark datasets.
  • Performance was compared against existing feature selection models in the literature.
  • The novel approach demonstrated superior performance in terms of both scalability and accuracy.

Conclusions:

  • The integrated framework effectively handles high-dimensional big data.
  • The combination of rough sets and differential evolution (DE) provides an accurate and scalable feature selection solution.
  • This research contributes a valuable tool for data scientists dealing with dimensionality issues.