Paper Info
Title
Possibilistic classifiers for uncertain numerical data
Abstract
In many real-world problems, input data may be pervaded with uncertainty. Naive possibilistic classifiers have been proposed as a counterpart to Bayesian classifiers to deal with classification tasks in presence of uncertainty. Following this line here, we extend possibilistic classifiers, which have been recently adapted to numerical data, in order to cope with uncertainty in data representation. We consider two types of uncertainty: i) the uncertainty associated with the class in the training set, which is modeled by a possibility distribution over class labels, and ii) the imprecision pervading attribute values in the testing set represented under the form of intervals for continuous data. We first adapt the possibilistic classification model, previously proposed for the certain case, in order to accommodate the uncertainty about class labels. Then, we propose an extension principle-based algorithm to deal with imprecise attribute values. The experiments reported show the interest of possibilistic classifiers for handling uncertainty in data. In particular, the probability-to-possibility transform-based classifier shows a robust behavior when dealing with imperfect data.
Year
Venue
Keywords
2011
ECSQARU
imperfect data,input data,classification task,numerical data,possibilistic classification model,naive possibilistic classifier,continuous data,possibilistic classifier,class label,uncertain numerical data,data representation
Field
DocType
Citations 
Training set,Data mining,External Data Representation,Imperfect,Computer science,Possibility theory,Uncertainty analysis,Artificial intelligence,Classifier (linguistics),Possibility distribution,Machine learning,Bayesian probability
Conference
2
PageRank 
References 
Authors
0.37
16
4
Name
Order
Citations
PageRank
Myriam Bounhas1838.70
Henri Prade2105491445.02
Mathieu Serrurier326726.94
Khaled Mellouli472984.09