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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.
Journal of Grid Computing
|October 12, 2020
Summary
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.
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.
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