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PToPI: A Comprehensive Review, Analysis, and Knowledge Representation of Binary Classification Performance
Gürol Canbek1,2, Tugba Taskaya Temizel2, Seref Sagiroglu3
1Pointr, Ankara, Turkey.
SN Computer Science
|October 21, 2022
Summary
This study introduces novel concepts and the Periodic Table of Performance Instruments (PToPI) to systematically evaluate and understand numerous machine learning classification performance measures and metrics. PToPI aids researchers in selecting appropriate instruments for specific classification tasks.
Area of Science:
- Computer Science
- Machine Learning
- Data Science
Background:
- Numerous performance evaluation instruments exist for machine learning classification, but conventional usage is limited.
- Existing terminology and evaluation methods lack clarity and systematic organization.
- A survey of 78 mobile-malware classification studies revealed issues in performance evaluation.
Purpose of the Study:
- To review and describe performance evaluation instruments using novel concepts and clarified terminology.
- To propose new concepts for identifying characteristics, similarities, and differences among instruments.
- To categorize instruments into 'performance measures' and 'performance metrics' for the first time.
Main Methods:
- Survey of 78 mobile-malware classification studies to identify issues.
- Proposal of novel concepts (canonical form, geometry, duality, etc.) to analyze instruments.
- Development of the Periodic Table of Performance Instruments (PToPI) as a knowledge representation tool.
Main Results:
- Introduction of novel concepts to reveal intrinsic properties and relationships of instruments.
- Categorization of instruments into performance measures and metrics.
- Creation of PToPI, visualizing 69 instruments (29 measures, 28 metrics) with their properties and dependencies.
Conclusions:
- PToPI aids in understanding instrument properties, similarities, and differences.
- The proposed concepts and PToPI facilitate systematic classification performance evaluation.
- Researchers can better comprehend, use, and select appropriate performance instruments for specific problems.
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