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• W3113236486 abstract "In this article, we propose a method for evaluating feature ranking algorithms. A feature ranking algorithm estimates the importance of descriptive features when predicting the target variable, and the proposed method evaluates the correctness of these importance values by computing the error measures of two chains of predictive models. The models in the first chain are built on nested sets of top-ranked features, while the models in the other chain are built on nested sets of bottom ranked features. We investigate which predictive models are appropriate for building these chains, showing empirically that the proposed method gives meaningful results and can detect differences in feature ranking quality. This is first demonstrated on synthetic data, and then on several real-world classification benchmark problems." @default.
• W3113236486 created "2020-12-21" @default.
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• W3113236486 date "2020-12-07" @default.
• W3113236486 modified "2023-10-02" @default.
• W3113236486 title "Error curves for evaluating the quality of feature rankings" @default.
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• W3113236486 doi "https://doi.org/10.7717/peerj-cs.310" @default.
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• W3113236486 hasPublicationYear "2020" @default.
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