ArticleInternational journal of machine learning and cybernetics2023
BenchMetrics Prob: benchmarking of probabilistic error/loss performance evaluation instruments for binary classification problems.
Article in International journal of machine learning and cybernetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- A deep learning model for assistive decision-making during robot-aided rehabilitation therapies based on therapists' demonstrations.Journal of neuroengineering and rehabilitation · 2025Article
- HasLoss: a novel Hassanat distance-based loss functions for binary classification.Frontiers in artificial intelligence · 2025Article
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Abstract
Probabilistic error/loss performance evaluation instruments that are originally used for regression and time series forecasting are also applied in some binary-class or multi-class classifiers, such as artificial neural networks. This study aims to systematically assess probabilistic instruments for binary classification performance evaluation using a proposed two-stage benchmarking method called BenchMetrics Prob. The method employs five criteria and fourteen simulation cases based on hypothetical classifiers on synthetic datasets. The goal is to reveal specific weaknesses of performance instruments and to identify the most robust instrument in binary classification problems. The BenchMetrics Prob method was tested on 31 instrument/instrument variants, and the results have identified four instruments as the most robust in a binary classification context: Sum Squared Error (
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