Classification using Artificial Neural Network Optimized with Bat Algorithm
Narender Kumar1, Dharmender Kumar2

1Mr. Narender Kumar*, Computer Science Engineering,, GJUS&T, Hisar, India.
2Dharmender Kumar, Computer Science Engineering,, GJUS&T, Hisar, India.
Manuscript received on December 15, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 696-700 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8378019320/2020©BEIESP | DOI: 10.35940/ijitee.C8378.019320
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Abstract: In machine learning, there are two approaches: supervised and unsupervised learning. Classification is a technique which falls under the supervised learning. Out of many classification models, the most popularly used is the Artificial Neural Network. While neural networks work fine in classification and training a machine, the accuracy of the result might still be under question. To improve the accuracy and speed of result, the optimisation of artificial neural network is done. For this, ANN can be hybridised with a metaheuristic algorithm known as the Bat Algorithm. The benefits of optimising a neural network are mainly the improvement in accuracy of classification, interpretation of the data, reduction in cost and time consumption for getting accurate results etc. In present paper, a comparison between the results of an ANNBackpropagation model and the proposed ANN-Bat model is done for medical diagnosis. The results were in the favour of the ANN-Bat approach which was significant in reducing the time taken to yield an output as well as the accuracy. 
Keywords: Artificial Neural Networks, Bat Algorithm, Back Propagation, Classification, Optimisation.
Scope of the Article: Classification