Retracted: Naive Credal Classifier for Uncertain Data Classification

  • S. Sai Satyanarayana Reddy
  • G. V. Suresh
  • T. Raghunadha Reddy
  • B. Vishnu Vardhan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 199)

Abstract

Data uncertainty due to various causes, including imprecise measurement, network latency, out-dated sources and sampling errors, is common in real-world applications. Data Analysis applications are typical in collecting and accumulating large amounts of uncertain data. This attracted more and more database community to analyze and resolve the uncertainty incured in the large data sets. We, in this article, present a naive classifier, which is a Set-Valued counterpart of Naive Bayes that is extended to a general and flexible treatment of incomplete data, yielding to a new classifier called Naïve Credal Classifier. Naïve Credal Classifieris an application on closed and convex sets of probability distributions called Credal sets, of uncertainty measures. The Naïve Credal Classifier extends the discrete Naive Bayes classifier to imprecise probabilities and also models both prior ignorance and ignorance about the likelihood by sets of probability distributions. This is a new means to deal with uncertain data sets that departs significantly from most established conventional classification methods. Experimental results show that proposed model exhibits reasonable accuracy performance in classification on uncertain data.

Keywords

Uncertain Data Naive Bayesian Classifier Credal Classifier Data Mining 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • S. Sai Satyanarayana Reddy
    • 1
  • G. V. Suresh
    • 1
  • T. Raghunadha Reddy
    • 2
  • B. Vishnu Vardhan
    • 3
  1. 1.LBRCEMyalvaramIndia
  2. 2.SIETNarsapurIndia
  3. 3.JNTUCEJJagitiyalaIndia

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