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Linear Approximation of F-Measure for the Performance Evaluation of Classification Algorithms on Imbalanced Data Sets
Tzu Tsung Wong
Center for Innovative Fintech Business Models
Institute of Information Management
Research output
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Contribution to journal
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Article
›
peer-review
13
Citations (Scopus)
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Dive into the research topics of 'Linear Approximation of F-Measure for the Performance Evaluation of Classification Algorithms on Imbalanced Data Sets'. Together they form a unique fingerprint.
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Keyphrases
Performance Evaluation
100%
Imbalanced Data
100%
Classification Algorithms
100%
F-measure
100%
Linear Approximation
100%
Approximation Approach
40%
Sampling Distribution
40%
Popular
20%
Evaluation Method
20%
Evaluating Performance
20%
Harmonic Mean
20%
K-fold Cross-validation
20%
Joint Distribution
20%
Bivariate Normal Distribution
20%
Computer Science
Performance Evaluation
100%
Approximation (Algorithm)
100%
Imbalanced Data
100%
Classification Algorithm
100%
Sampling Distribution
40%
Normal Distribution
20%
Fold Cross Validation
20%
Joint Distribution
20%
Mathematics
Linear Approximation
100%
Sampling Distribution
66%
Cross-Validation
33%
Joint Distribution
33%
Bivariate Normal Distribution
33%
Harmonic Mean
33%