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- W3200409464 abstract "In machine learning, the performance of a classifier depends on both the classifier model and the separability/complexity of datasets. To quantitatively measure the separability of datasets, in this study, we propose an intrinsic measure – the Distance-based Separability Index (DSI), which is independent of the classifier model. We then formally show that the DSI can indicate whether the distributions of datasets are identical for any dimensionality. DSI can measure separability of datasets because we consider the situation in which different classes of data are mixed in the same distribution to be the most difficult for classifiers to separate. And, DSI is verified to be an effective separability measure by comparing it to state-of-the-art separability/complexity measures using synthetic datasets and real datasets (CIFAR-10/100). Having demonstrated the DSI’s ability to compare distributions of samples, our other studies show that it can be used in other separability-based applications, such as measuring the performance of generative adversarial networks (GANs) and evaluating the results of clustering methods." @default.
- W3200409464 created "2021-09-27" @default.
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- W3200409464 date "2022-04-05" @default.
- W3200409464 modified "2023-10-02" @default.
- W3200409464 title "A novel intrinsic measure of data separability" @default.
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- W3200409464 doi "https://doi.org/10.1007/s10489-022-03395-6" @default.
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