Paper Info
Title
Imprecise regression based on possibilistic likelihood
Abstract
Machine learning, and more specifically regression, usually focuses on the search for a precise model, when precise data are available. It is well-known that the model thus found may not exactly describe the target concept, due to the existence of learning biases. So, we are interested in a learning process that accounts also for the uncertainty around the predicted value which should not be illusionary precise. The goal of imprecise regression is to find a model that offers a good trade-off between faithfulness w.r.t. data and (meaningful) precision. The function that is learnt associates, to each input vector, a possibility distribution which represents a family of probability distributions. Based on this interpretation of a possibilistic distribution, we define the notion of possibilistic likelihood. Then, we propose a framework of imprecise regression based on the previous notion and a particle swarm optimization process. This approach takes advantage of the capability of triangular possibility distributions to approximate any unimodal probability distribution from above. We illustrate our approach with a generated dataset.
Year
DOI
Venue
2011
10.1007/978-3-642-23963-2_35
SUM
Keywords
Field
DocType
possibilistic likelihood,imprecise regression,unimodal probability distribution,possibilistic distribution,triangular possibility distribution,precise data,precise model,machine learning,particle swarm optimization process,probability distribution,possibility distribution
Particle swarm optimization,Data mining,Regression,Computer science,Probability distribution,Artificial intelligence,Possibility distribution,Machine learning
Conference
Volume
ISSN
Citations 
6929
0302-9743
3
PageRank 
References 
Authors
0.44
6
2
Name
Order
Citations
PageRank
Mathieu Serrurier126726.94
Henri Prade2105491445.02