论文信息 - A Comparative Study of Different Machine Learning Approaches for Decision Making

A Comparative Study of Different Machine Learning Approaches for Decision Making

In this paper, several neural network and statistical learning approaches are proposed that learn to make human like decisions for the job assignment problem of the US Navy. Comparison study of Feedforward Neural Networks (FFNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM) and Adaptive Bayes (AB) classifier with Generalized Estimation Equation (GEE) is provided. Although Support Vector Machine provided the highest decision making rate, other methods also offer various unique advantages. In spite of the high correlation naturally present in the data, with the use of post-processing results were further improved. Key-Words: Feedforward Neural Network, Adaptive Neuro-Fuzzy Inference System, Support Vector Machine, Adaptive Bayes Classifier

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