WB Score: A Novel Methodology for Visual Classifier Selection in Increasingly Noisy Datasets

This article addresses the challenges of selecting robust classifiers with increasing noise levels in real-world scenarios. We propose the WB Score methodology, which enables the identification of reliable classifiers for deployment in noisy environments. The methodology addresses four significant c...

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Bibliographic Details
Published in:Eng (Basel, Switzerland) Switzerland), 2023-12, Vol.4 (4), p.2497-2513
Main Authors: Billa, Wagner S., Negri, Rogério G., Santos, Leonardo B. L.
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Classification
classifier selection
Classifiers
computational classification
Datasets
Decision making
Machine learning
Methodology
Methods
Noise
Noise levels
noise robustness
Robustness
visual decision-making
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Summary:This article addresses the challenges of selecting robust classifiers with increasing noise levels in real-world scenarios. We propose the WB Score methodology, which enables the identification of reliable classifiers for deployment in noisy environments. The methodology addresses four significant challenges that are commonly encountered: (i) Ensuring classifiers possess robustness to noise; (ii) Overcoming the difficulty of obtaining representative data that captures real-world noise; (iii) Addressing the complexity of detecting noise, making it challenging to differentiate it from natural variations in the data; and (iv) Meeting the requirement for classifiers capable of efficiently handling noise, allowing prompt responses for decision-making. WB Score provides a comprehensive approach for classifier assessment and selection to address these challenges. We analyze five classic datasets and one customized flooding dataset in São Paulo. The results demonstrate the practical effect of using the WB Score methodology is the enhanced ability to select robust classifiers for datasets in noisy real-world scenarios. Compared with similar techniques, the improvement centers around providing a visual and intuitive output, enhancing the understanding of classifier resilience against noise, and streamlining the decision-making process.
ISSN:2673-4117
2673-4117
DOI:10.3390/eng4040142