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Hadoop neural network for parallel and distributed feature selection.

Victoria J Hodge1, Simon O'Keefe1, Jim Austin1

  • 1Advanced Computer Architecture Group, Department of Computer Science, University of York, York, YO10 5GH, UK.

Neural Networks : the Official Journal of the International Neural Network Society
|September 26, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a Hadoop-based neural network for efficient, parallel feature selection in Big Data. The framework leverages binary associative memory neural networks for optimized Big Data analysis.

Keywords:
Binary neural networkDistributedFeature selectionHadoopMapReduceParallel

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Big Data Analytics

Background:

  • Feature selection is crucial for Big Data analysis but computationally intensive.
  • Existing feature selection methods have limitations in scalability and efficiency.
  • Hadoop offers a paradigm for parallel and distributed processing.

Purpose of the Study:

  • To develop a theoretical basis for a Hadoop-based neural network for parallel and distributed feature selection.
  • To implement and compare five feature selection algorithms within this framework.
  • To optimize Big Data analysis by efficiently identifying relevant features.

Main Methods:

  • Utilized a binary associative memory neural network architecture.
  • Embedded the neural network framework within Hadoop YARN for distributed processing.
  • Developed five distinct feature selection algorithms adaptable to the framework.
  • Implemented parallel processing for subtasks and multiple feature selectors.

Main Results:

  • Demonstrated the feasibility of parallel and distributed feature selection using the proposed framework.
  • Showcased the ability to process multiple feature selectors simultaneously for comparison.
  • Identified commonalities among feature selectors to reduce redundant computations.
  • Achieved efficient identification of optimal features for large, high-dimensional datasets.

Conclusions:

  • The Hadoop-based neural network provides an efficient and flexible solution for Big Data feature selection.
  • Leveraging binary associative memory networks within Hadoop enhances scalability and performance.
  • The framework facilitates the selection of optimal features and algorithms for complex datasets.