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Attribute Ranking with Bipolar Information


In this paper, we place ourselves in a machine learning context and we tackle the problem of the ranking of attributes through bipolar information. From a classical training set, bipolar sets (Atanassov intuitionistic fuzzy sets or interval-valued fuzzy sets) are constructed and could thus be used with bipolar information measures, such as entropies, in order to produce a novel approach to rank attributes. With such an approach, new means to highlight the lack of knowledge associated with the distribution of attribute values are offered.
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hal-03679648 , version 1 (26-05-2022)


  • HAL Id : hal-03679648 , version 1


Christophe Marsala. Attribute Ranking with Bipolar Information. Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU'2022), Jul 2022, Milan, Italy. ⟨hal-03679648⟩
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