Intrinsic Dimensionality as a Model-Free Measure of Class Imbalance
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912836320493568 |
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| author | Eser, Çağrı Baltacı, Zeynep Sonat Akbaş, Emre Kalkan, Sinan |
| author_facet | Eser, Çağrı Baltacı, Zeynep Sonat Akbaş, Emre Kalkan, Sinan |
| contents | Imbalance in classification tasks is commonly quantified by the cardinalities of examples across classes. This, however, disregards the presence of redundant examples and inherent differences in the learning difficulties of classes. Alternatively, one can use complex measures such as training loss and uncertainty, which, however, depend on training a machine learning model. Our paper proposes using data Intrinsic Dimensionality (ID) as an easy-to-compute, model-free measure of imbalance that can be seamlessly incorporated into various imbalance mitigation methods. Our results across five different datasets with a diverse range of imbalance ratios show that ID consistently outperforms cardinality-based re-weighting and re-sampling techniques used in the literature. Moreover, we show that combining ID with cardinality can further improve performance. Our code and models are available at https://github.com/cagries/IDIM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_10475 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Intrinsic Dimensionality as a Model-Free Measure of Class Imbalance Eser, Çağrı Baltacı, Zeynep Sonat Akbaş, Emre Kalkan, Sinan Machine Learning Computer Vision and Pattern Recognition Imbalance in classification tasks is commonly quantified by the cardinalities of examples across classes. This, however, disregards the presence of redundant examples and inherent differences in the learning difficulties of classes. Alternatively, one can use complex measures such as training loss and uncertainty, which, however, depend on training a machine learning model. Our paper proposes using data Intrinsic Dimensionality (ID) as an easy-to-compute, model-free measure of imbalance that can be seamlessly incorporated into various imbalance mitigation methods. Our results across five different datasets with a diverse range of imbalance ratios show that ID consistently outperforms cardinality-based re-weighting and re-sampling techniques used in the literature. Moreover, we show that combining ID with cardinality can further improve performance. Our code and models are available at https://github.com/cagries/IDIM. |
| title | Intrinsic Dimensionality as a Model-Free Measure of Class Imbalance |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.10475 |