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Low-precision feature selection on microarray data: an information theoretic approach.

Laura Morán-Fernández1, Verónica Bolón-Canedo2, Amparo Alonso-Betanzos2

  • 1CITIC, Universidade da Coruña, A Coruña, Spain. laura.moranf@udc.es.

Medical & Biological Engineering & Computing
|March 22, 2022
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Summary
This summary is machine-generated.

Low-precision machine learning algorithms for the Internet of Things (IoT) maintain accuracy on DNA microarray data. Using 8-bit or 16-bit representations significantly reduces computational load without impacting classification results.

Keywords:
ClassificationEdge computingFeature selectionInternet of ThingsLow precisionMicroarray dataMutual information

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Area of Science:

  • Computer Science
  • Bioinformatics
  • Machine Learning

Background:

  • The proliferation of Internet of Things (IoT) devices necessitates efficient on-device computation.
  • IoT devices have limited computational power, posing a challenge for complex machine learning tasks.
  • Edge computing with IoT devices can reduce network congestion by processing data near the source.

Purpose of the Study:

  • To investigate the feasibility of using low-precision machine learning algorithms for IoT applications.
  • To evaluate the impact of reduced precision on the accuracy of mutual information-based feature selection.
  • To apply these algorithms to DNA microarray datasets for classification tasks.

Main Methods:

  • Implementation of mutual information-based feature selection algorithms with reduced numerical precision (16-bit and 8-bit).
  • Application of these low-precision algorithms to DNA microarray datasets.
  • Evaluation of classification performance using the selected features.

Main Results:

  • Low-precision (16-bit and 8-bit) representations of the algorithms achieved comparable classification accuracy to higher-precision versions.
  • Significant reduction in computational requirements is possible without substantial loss of performance.
  • Feature selection using low-precision mutual information is effective for DNA microarray data analysis.

Conclusions:

  • Reduced-precision machine learning algorithms are viable for resource-constrained IoT devices.
  • This approach enables efficient pattern recognition and decision-making at the edge.
  • The findings support the deployment of advanced analytics on IoT devices for applications like genomic data analysis.