Case-Based Predictions: An Axiomatic Approach to Prediction, Classification and Statistical Learning

Itzhak Gilboa (1) , David Schmeidler

1 GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC

The book presents an axiomatic approach to the problems of prediction, classification, and statistical learning. Using methodologies from axiomatic decision theory, and, in particular, the authors' case-based decision theory, the present studies attempt to ask what inductive conclusions can be derived from existing databases. It is shown that simple consistency rules lead to similarity-weighted aggregation, akin to kernel-based methods. It is suggested that the similarity function be estimated from the data. The incorporation of rule-based reasoning is discussed.

Keywords: Case-Based Predictions; Axiomatic Approach; Prediction; Classification; Statistical Learning

Domains: Economy and decision science

Itzhak Gilboa, David Schmeidler. Case-Based Predictions: An Axiomatic Approach to Prediction, Classification and Statistical Learning. World Scientific Publishers, pp.NC, 2011, Economic Theory Series. ⟨hal-00756301⟩