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Jan 27, 2020 · In this paper, we explore the possibility of using Rényi min-entropy instead. In particular, we propose an algorithm based on a notion of conditional Rényi min ...
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Sep 8, 2024 · We prove that in general the two approaches are incomparable, in the sense that we show that we can construct datasets on which the R\'enyi- ...
Jan 27, 2020 · All the information-theoretic approaches to feature selection that have been pro- posed are based, as far as we know, on Shannon entropy, with ...
Jan 27, 2020 · This paper proposes an algorithm based on a notion of conditional R\'enyi min-entropy that has been recently adopted in the field of ...
Feature selection in machine learning: Rényi min-entropy vs Shannon entropy · Multiclass EEG signal classification utilizing Rényi min-entropy-based feature ...
Aug 16, 2018 · We evaluate our method on 2 classifiers and 3 datasets, and we show that it compares favorably with the corresponding one based on Shannon ...
Jun 16, 2020 · This paper proposes a novel feature selection method utilizing Rényi min-entropy-based algorithm for achieving a highly efficient brain–computer interface (BCI ...
We evaluate our method on two classifiers and three datasets, and we show that it compares favorably with the corresponding one based on Shannon entropy.
Feature selection. Cross entropy is used to select significant features of binary values from highly imbalanced large datasets via a framework called FMC ...
Apr 16, 2024 · In principle, Shannon and Rényi entropies were compared experimentally, and the findings indicated that neither entropy calculation method ...