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A Review of Supervised Classification based on Contrast Patterns: Applications, Trends, and Challenges.

Octavio Loyola-González1, Miguel Angel Medina-Pérez2, Kim-Kwang Raymond Choo3

  • 1Tecnologico de Monterrey, Reserva Territorial Atlixcáyotl, Vía Atlixcáyotl No. 2301, Puebla, 72453 Mexico.

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Summary
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This review explores supervised classification using Contrast Patterns (CP), highlighting their accuracy and interpretability. We categorize applications and identify future research directions in pattern recognition.

Keywords:
Contrast patternsReviewSupervised classificationTaxonomy

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Area of Science:

  • Computer Science
  • Pattern Recognition
  • Machine Learning

Background:

  • Supervised classification is crucial in pattern recognition.
  • Contrast Patterns (CP) offer a promising approach due to their accuracy and interpretability.
  • Existing literature shows growing interest in CP-based classification.

Purpose of the Study:

  • To conduct an in-depth review of 105 articles on CP-based supervised classification.
  • To present a taxonomy of application domains for CP-based classification.
  • To perform a scientometric study and identify future research opportunities.

Main Methods:

  • Systematic literature survey of 105 relevant articles.
  • Analysis of CP-based supervised classification techniques.
  • Development of a taxonomy for application domains.
  • Scientometric analysis of the research landscape.

Main Results:

  • Identified and categorized diverse application domains of CP-based supervised classification.
  • Provided a comprehensive overview of the current state of research.
  • Conducted a scientometric study to map research trends and impact.

Conclusions:

  • Contrast Patterns (CP) represent a significant and versatile tool in supervised classification.
  • The review establishes a foundation for understanding CP applications and their impact.
  • Future research should focus on emerging opportunities identified in the scientometric analysis.