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Qualitative Inference in Possibilistic Option Decision Trees
Book chapter   Peer reviewed

Qualitative Inference in Possibilistic Option Decision Trees

Ilyes Jenhani, Zied Elouedi, Nahla Ben Amor and Khaled Mellouli
Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pp.944-955
Lecture Notes in Computer Science, Springer Berlin Heidelberg
2005
DOI: https://doi.org/10.1007/11518655_79

Abstract

Decision Node Decision Tree Decision Tree Algorithm Gain Ratio Possibility Distribution
This paper presents a classification technique using possibility theory, namely the possibilistic option decision trees (PODT) which offers a more flexible building procedure by selecting more than one attribute in each decision node. Then, a classification method, using the PODT, to determine the class value of instances characterized by uncertain/missing attributes is proposed.

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