Some Experimental Results on Learning Probabilistic and Possibilistic Networks with Different Evaluation Measures
نویسندگان
چکیده
A large part of recent research on probabilistic and possibilistic inference networks has been devoted to learning them from data. In this paper we discuss two search methods and several evaluation measures usable for this task. We consider a scheme for evaluating induced networks and present experimental results obtained from an application of INES (Induction of NEtwork Structures), a prototype implementation of the described methods and measures.
منابع مشابه
Evaluation Measures for Learning Probabilistic and Possibilistic Networks
Evidence propagation in inference networks, probabilistic or possibilistic, can be done in two different ways — using a product/sum scheme or using a minimum/maximum scheme — depending on the type of answers one expects from the network. Usually the first is seen in connection with probabilistic, the second in connection with possibilistic reasoning, although we argue that both schemes are appl...
متن کاملSome Experimental Results on Learning Probabilistic and Possibilistic Networks with Diierent Evaluation Measures
A large part of recent research on probabilistic and possi-bilistic inference networks has been devoted to learning them from data. In this paper we discuss two search methods and several evaluation measures usable for this task. We consider a scheme for evaluating induced networks and present experimental results obtained from an application of INES (Induction of NEtwork Structures), a prototy...
متن کاملLearning Possibilistic Networks with a Global Evaluation Method
Inference networks, probabilistic as well as possibilistic, are popular techniques to make reasoning in multi-dimensional domains feasible. Since constructing them by hand can be tedious and time consuming, a large part of recent research has been devoted to learning inference networks from data. Most of the proposed methods are based on local, i.e. single hyperedge evaluation. In this paper we...
متن کاملPossibilistic networks parameter learning: Preliminary empirical comparison
Like Bayesian networks, possibilistic ones compactly encode joint uncertainty representations over a set of variables. Learning possibilistic networks from data in general and from imperfect or scarce data in particular, has not received enough attention. Indeed, only few works deal with learning the structure and the parameters of a possibilistic network from a dataset. This paper provides a p...
متن کاملCompiling Possibilistic Networks: Alternative Approaches to Possibilistic Inference
Qualitative possibilistic networks, also known as min-based possibilistic networks, are important tools for handling uncertain information in the possibility theory framework. Despite their importance, only the junction tree adaptation has been proposed for exact reasoning with such networks. This paper explores alternative algorithms using compilation techniques. We first propose possibilistic...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1997