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Communication Dans Un Congrès Année : 2021

Decision-making from partial test instances by active feature querying

Résumé

We consider a classification problem in which test instances are not available as complete feature vectors, but must rather be uncovered by repeated queries to an oracle. We have a limited budget of queries: the problem is then to find the best features to ask the oracle for. We consider here a strategy where features are uncovered one by one, so as to maximize the separation between the classes. Once an instance has been uncovered, the distribution of the remaining instances is updated according to the observation. Experiments on synthetic and real data show that our strategy remains reasonably accurate when a decision must be made based on a limited amount of observed features. We briefly discuss the case of imprecise answers, and list out the many problems arising in this case.
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Dates et versions

hal-03506395 , version 1 (02-01-2022)

Identifiants

  • HAL Id : hal-03506395 , version 1

Citer

Benjamin Quost. Decision-making from partial test instances by active feature querying. 12th International Symposium on Imprecise Probability: Theories and Applications (ISIPTA 2021), Jul 2021, Granada, Spain. pp.264-272. ⟨hal-03506395⟩
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