Neural Networks
Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Mathematical Programming for Data Mining: Formulations and Challenges
INFORMS Journal on Computing
Support Vector Machine Soft Margin Classifiers: Error Analysis
The Journal of Machine Learning Research
A mixed integer optimisation model for data classification
Computers and Industrial Engineering
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Probabilistic Approaches For Credit Screening And Bankruptcy Prediction
International Journal of Intelligent Systems in Accounting and Finance Management
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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.