Abstract
Classification of data is a significant method of data analysis that can be used for intelligent decision making and neural networks are vital contrivances of such classification. Several meta-heuristic algorithms based neural network models such as genetic algorithm (GA) and differential evolution (DE) based MLP are efficiently implemented for this task. However these methods are trapped at local optima. To overcome such limitations, firefly algorithm (FA) and bird mating optimization (BMO) based MLP techniques have been proposed in the paper and tested over several bio-medical datasets like thyroid, hepatitis and heart diseases for classification. The result shows efficient classification of different patients into their diseases categories according to the data obtained from different pathological test.
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Behera, N.K.S., Routray, A.R., Nayak, J., Behera, H.S. (2015). Bird Mating Optimization Based Multilayer Perceptron for Diseases Classification. In: Jain, L., Behera, H., Mandal, J., Mohapatra, D. (eds) Computational Intelligence in Data Mining - Volume 3. Smart Innovation, Systems and Technologies, vol 33. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2202-6_27
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DOI: https://doi.org/10.1007/978-81-322-2202-6_27
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