The art of misclassification: too many classes, not enough points
Abstract Classification is a ubiquitous and fundamental problem in artificial intelligence and machine learning, with extensive efforts dedicated to developing more powerful classifiers and larger datasets. However, the classification task is ultimately constrained by the intrinsic properties of dat...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-07-01
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| Series: | EPJ Data Science |
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| Online Access: | https://doi.org/10.1140/epjds/s13688-025-00565-7 |
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| _version_ | 1849332882770755584 |
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| author | Mario Franco Gerardo Febres Nelson Fernández Carlos Gershenson |
| author_facet | Mario Franco Gerardo Febres Nelson Fernández Carlos Gershenson |
| author_sort | Mario Franco |
| collection | DOAJ |
| description | Abstract Classification is a ubiquitous and fundamental problem in artificial intelligence and machine learning, with extensive efforts dedicated to developing more powerful classifiers and larger datasets. However, the classification task is ultimately constrained by the intrinsic properties of datasets, independently of computational power or model complexity. In this work, we introduce a formal entropy-based measure of classifiability, which quantifies the inherent difficulty of a classification problem by assessing the uncertainty in class assignments given feature representations. This measure captures the degree of class overlap and aligns with human intuition, serving as an upper bound on classification performance for classification problems. Our results establish a theoretical limit beyond which no classifier can improve the classification accuracy, regardless of the architecture or amount of data, in a given problem. Our approach provides a principled framework for understanding when classification is inherently fallible and fundamentally ambiguous. |
| format | Article |
| id | doaj-art-3d6d9ac9bb3b407cbbe43c53337f8061 |
| institution | Kabale University |
| issn | 2193-1127 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EPJ Data Science |
| spelling | doaj-art-3d6d9ac9bb3b407cbbe43c53337f80612025-08-20T03:46:04ZengSpringerOpenEPJ Data Science2193-11272025-07-0114112210.1140/epjds/s13688-025-00565-7The art of misclassification: too many classes, not enough pointsMario Franco0Gerardo Febres1Nelson Fernández2Carlos Gershenson3School of Systems Science and Industrial Enginnering, Binghamton UniversitySchool of Systems Science and Industrial Enginnering, Binghamton UniversitySchool of Systems Science and Industrial Enginnering, Binghamton UniversitySchool of Systems Science and Industrial Enginnering, Binghamton UniversityAbstract Classification is a ubiquitous and fundamental problem in artificial intelligence and machine learning, with extensive efforts dedicated to developing more powerful classifiers and larger datasets. However, the classification task is ultimately constrained by the intrinsic properties of datasets, independently of computational power or model complexity. In this work, we introduce a formal entropy-based measure of classifiability, which quantifies the inherent difficulty of a classification problem by assessing the uncertainty in class assignments given feature representations. This measure captures the degree of class overlap and aligns with human intuition, serving as an upper bound on classification performance for classification problems. Our results establish a theoretical limit beyond which no classifier can improve the classification accuracy, regardless of the architecture or amount of data, in a given problem. Our approach provides a principled framework for understanding when classification is inherently fallible and fundamentally ambiguous.https://doi.org/10.1140/epjds/s13688-025-00565-7Classification limitsData limitsEntropy-based measureMachine learningArtificial intelligence |
| spellingShingle | Mario Franco Gerardo Febres Nelson Fernández Carlos Gershenson The art of misclassification: too many classes, not enough points EPJ Data Science Classification limits Data limits Entropy-based measure Machine learning Artificial intelligence |
| title | The art of misclassification: too many classes, not enough points |
| title_full | The art of misclassification: too many classes, not enough points |
| title_fullStr | The art of misclassification: too many classes, not enough points |
| title_full_unstemmed | The art of misclassification: too many classes, not enough points |
| title_short | The art of misclassification: too many classes, not enough points |
| title_sort | art of misclassification too many classes not enough points |
| topic | Classification limits Data limits Entropy-based measure Machine learning Artificial intelligence |
| url | https://doi.org/10.1140/epjds/s13688-025-00565-7 |
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