Abstract
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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© 2005 Springer-Verlag Berlin Heidelberg
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Jenhani, I., Elouedi, Z., Amor, N.B., Mellouli, K. (2005). Qualitative Inference in Possibilistic Option Decision Trees. In: Godo, L. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2005. Lecture Notes in Computer Science(), vol 3571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11518655_79
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DOI: https://doi.org/10.1007/11518655_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27326-4
Online ISBN: 978-3-540-31888-0
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