Applying WEKA towards Machine Learning With Genetic Algorithm and Back-propagation Neural Networks
Zeeshan Ahmed1,2* and Saman Zeeshan2
1Department of Neurobiology and Genetics, Biocenter, University of Wuerzburg, Germany
2Department of Bioinformatics, Biocenter, University of Wuerzburg, Germany
- *Corresponding Author:
- Zeeshan Ahmed
Department of Neurobiology and Genetics
University of Wuerzburg, Germany
E-mail: [email protected]
Received date: May 20, 2014; Accepted date: August 26, 2014; Published date: September 02,2014
Citation: Ahmed Z, Zeeshan S (2014) Applying WEKA towards Machine Learning With Genetic Algorithm and Back-propagation Neural Networks. J Data Mining Genomics Proteomics 5:157. doi:10.4172/2153-0602.1000157
Copyright: © 2014 Ahmed Z, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Machine learning aims of facilitating complex system data analysis, optimization,classification and predictionwith the use of different mathematical and statistical algorithms. In this research, we are interested in establishing the process of estimating best optimal input parameters to train networks. Using WEKA, this paper implements a classifier with Back-propagation Neural Networks and Genetic Algorithm towards efficient data classification and optimization.The implemented classifier is capable of reading and analyzing a number of populations in giving datasets, and based on the identified population it estimates kinds of species in a population, hidden layers, momentum, accuracy, correct and incorrect instances.