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A New Naïve Style Possibilistic Network Classifier

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 135)

Abstract

This paper proposes a new approach of classification under the possibilistic network (PN) framework with Tree Augmented Naïve Bayes Network classifier (TAN), which combines the advantages of both PN and TAN. The classifier is built from a training set where instances can be expressed by imperfect attributes and classes. A new operator, the possibilistic mean is designed to estimate the conditional possibility distributions of each attribute with imperfection, and the weight between two attributes given the class is determined by the conditioning specificity gain. Experiment has shown the efficiency of the new classifier in imperfect cases.

Keywords

Tree augmented naïve bayes network Possibility theory Possibilistic classifier Imperfect cases 

Notes

Acknowledgments

This work was supported by Doctor Subject Foundation of Heibei University of Science and Technology under Grant No. QD201051 and Scientific Research Plan of Hebei Education Department under Grant No. ZH2011243.

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Jianli Zhao
    • 1
  • Jiaomin Liu
    • 1
  • Zhaowei Sun
    • 2
  • Yan Zhao
    • 3
  1. 1.School of Electrical EngineeringHebei University of TechnologyTianjinChina
  2. 2.Hebei Education DepartmentStudent Information and Employment CenterShijiazhuangChina
  3. 3.Hebei University of Science and TechnologyShijiazhuangChina

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