A normalized probabilistic expectation-maximization neural network for minimizing bayesian misclassification cost risk

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Abstract

In this paper, we propose a normalized semi-supervised probabilistic expectation-maximization neural network (PEMNN) that minimizes Bayesian misclassification cost risk. Using simulated and real-world datasets, we compare the proposed PEMNN with supervised cost sensitive probabilistic neural network (PNN), discriminant analysis (DA), mathematical integer programming (MIP) model and support vector machines (SVM) for different misclassification cost asymmetries and class biases. The results of our experiments indicate that the PEMNN performs better when class data distributions are normal or uniform. However, when class data distribution is exponential the performance of PEMNN deteriorates giving slight advantage to competing MIP, DA, PNN and SVM techniques. For real-world data with non-parametric distributions and mixed decision-making attributes (continuous and categorical), the PEMNN outperforms the PNN.

Original languageEnglish (US)
Pages (from-to)417-431
Number of pages15
JournalNeural Processing Letters
Volume38
Issue number3
DOIs
StatePublished - Dec 1 2013

All Science Journal Classification (ASJC) codes

  • Software
  • Neuroscience(all)
  • Computer Networks and Communications
  • Artificial Intelligence

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