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Feature selection in machine learning: Rényi min-entropy vs Shannon entropy. Feature selection, in the context of machine learning, is the process of separating the highly predictive feature from those that might be irrelevant or redundant.
Jan 27, 2020
Aug 30, 2018 · We propose a new information-theoretic algorithm for ordering the features according to their relevance for classification.
Aug 16, 2018 · For feature selection we need the conditional version of Rényi min-entropy. Rényi did not define it, but there have been various proposals ...
International audienceWe consider the problem of feature selection, and we propose a new information-theoretic algorithm for ordering the features according ...
We consider the problem of feature selection, and we propose a new information-theoretic algorithm for ordering the features according to their relevance ...
The problem of feature selection is considered, and a new information-theoretic algorithm for ordering the features according to their relevance for ...
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 consider the problem of feature selection, and we propose a new information-theoretic algorithm for ordering the features according to their relevance ...
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Jan 27, 2020 · In general, indeed, with Rényi min-entropy the method tends to choose a feature which divides the classes in as many sets as possible.
Sep 8, 2024 · In this paper, we explore the possibility of using R\'enyi min-entropy instead. In particular, we propose an algorithm based on a notion of ...