Optimizing Feed Forward Neural Network Connection Weights Using Artificial Bee Colony Algorithm

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Provided by: International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE)
Topic: Security
Format: PDF
Feed Forward Neural networks (FFN) are one of many data mining analytical tools that can be utilized to make predictions for medical data. Model selection for a neural network entails various factors such as selection of the optimal number of hidden nodes, selection of the relevant input variables and selection of optimal connection weights. This paper presents the application of hybrid model that integrates Artificial Bee Colony (ABC) optimization and Feed Forward Neural network (FFN), where ABC is used to initialize and optimize the connection weights of FFN. Significant features identified by GA-CFS method are provided as input for both BPN and ABC-FFN.
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