The week's pick
Reseach Article
A Neuroph Approach for Classifying Ecoli Data
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 179 - Number 24 |
| Year of Publication: 2018 |
| Authors: Ejiofor C. I., Okon E. Uko |
10.5120/ijca2018916309
|
Ejiofor C. I., Okon E. Uko . A Neuroph Approach for Classifying Ecoli Data. International Journal of Computer Applications. 179, 24 ( Mar 2018), 19-26. DOI=10.5120/ijca2018916309
Abstract
Classification methods are used in handling classification problems, which usually exist when entities or objects needs to be assigned predefined groups or classes, perhaps based on attributes, parameters and values. This research paper provides a simplified description of neuroph classification using Ecoli data. The data were structured into training and testing datasets. The neuroph neural network architecture caters for 34 neurons: twelve input neurons (12), seventeen (17) hidden neurons and five (5) output neurons. The training accommodated approximately 185 iterations with a cumulative error of 6.8544 and an average error of 0.0367. The Total Mean Square Error (TMSE) obtained from testing the trained data gave an approximate value of 2.7372. This minima error showed the optimality in training and classification using Ecoli data.
References
- Ahmad, L. D. 2007, A method to compute distance between two categorical values of same attribute in unsupervised learning for categorical data set. Pattern Recognit. Lett. 28(1), 110–118.
- Anemone R. L. 2011, Race and biological diversity in humans, Race and Human Diversity: A Bio-cultural Approach. Upper Saddle River, NJ: Prentice Hall. 1–10. ISBN 0-131-83876-8.
- Ankerst, M. M. Breunig, H.P. Kriegel, J. Sander 1999, Optics: ordering points to identify the clustering structure, in ACM SIGMOD International Conference on Management of Data.
- Bilgisayar M. B. 2011, Classification of medical documents according to diseases, retrieved from: http://ieeexplore.ieee.org/document/7130164/.
- Centre for Disease and Control and Prevention; CDC 2017, Escherichia coli, retrieved online from http://cdc.com, October, 2017/
- Central Statistical Office CSO, 2014, what is Classification, retrieved http://www.cso.ie/en/methods/Classification/a//whatisaclassification/
- Kaplan, J. M. 2011 "'Race': What Biology Can Tell Us about a Social Construct". In: Encyclopedia of Life Sciences (ELS). John Wiley & Sons, Ltd: Chichester
- Karl J. Å., 2001, Control of Complex Systems, Springer.
- MedlinePlus 2017, “E.Coli Infection”, retrieved from https://medlineplus.gov/ ecoliinfections.html.
- Paul N. Finlay 2011, A classification of success factors for decision support systems, The Journal of Strategic Information Systems, 7(1), 53-70. Retrieved from https://www.predictiveanalyticstoday.com/neuroph/
- Sinha, R.; Liang, V.C.; Paredis, C. J. J.; Khosla, P.K. 2001."Modeling and Simulation Methods for Design of Engineering Systems", Journal of Computing and Information Science in Engineering. 1: 84–91. doi:10.1115/1.1344877. retrieved from http://neuroph.sourceforge.net/sample_projects.html
- Zoran S. 2011, Neural Networks on the NetBeans Platform, retrieved from http://www.oracle.com/technetwork/articles/java/nbneural-317387.html
Index Terms
Keywords