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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Editorial Topical Collection: "Explainable and Augmented Machine Learning for Biosignals and Biomedical Images"
Cosimo Ieracitano1, Mufti Mahmud2,3,4, Maryam Doborjeh5
1DICEAM Department, University Mediterranea of Reggio Calabria, Via Zehender, Feo di Vito, 89122 Reggio Calabria, Italy.
Sensors (Basel, Switzerland)
|December 23, 2023
Summary
Machine learning (ML), a part of artificial intelligence (AI), uses algorithms to let computer systems learn from data. This enables systems to improve performance on specific tasks without explicit programming.
Area of Science:
- Computer Science
- Artificial Intelligence
Background:
- Machine learning (ML) is a key subfield of artificial intelligence (AI).
- It focuses on algorithms and statistical models enabling systems to learn from data.
- This facilitates automatic adaptation to specific tasks through experience.
Discussion:
- ML algorithms enable systems to improve task performance.
- Learning from data is central to ML's adaptive capabilities.
- The field empowers computer systems with experience-driven enhancement.
Key Insights:
- Computer systems can adapt to tasks via ML.
- Data-driven learning is fundamental to AI advancement.
- ML algorithms provide a pathway for system self-improvement.
Outlook:
- Continued advancements in ML will drive AI innovation.
- Exploring new algorithms will expand ML applications.
- The integration of ML in various domains is expected to grow.
