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Naive possibilistic network classifiers
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AI
DOI：10.1016/j.fss.2009.01.009.png)
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Abstract
En 中文
Naive Bayesian network classifiers have proved their effectiveness to accomplish the classification task, even if they work under the strong assumption of independence of attributes in the context of the class node. However, as all of them are based on probability theory, they run into problems when they are faced with imperfection. This paper proposes a new approach of classification under the possibilistic framework with naive classifiers. To output the naive possibilistic network classifier, two procedures are studied namely the building phase, which deals with imperfect (imprecise/uncertain) dataset attributes and classes, and the classification phase, which is used to classify new instances that may be characterized by imperfect attributes. To improve the performance of our classifier, we propose two extensions namely selective naive possibilistic classifier and semi-naive possibilistic classifier. Experimental study has shown naive Bayes style possibilistic classifier, and is efficient in the imperfect case. (C) 2009 Elsevier B.V. All rights reserved.
Keywords:
Possibility theory
Classification
Naive Bayes classifier
Possibilistic classifier
Aggregation operators