The week's pick
Random Articles
Reseach Article
Analysis of Supervised Feature Selection Techniques on Animal Husbandry Dataset
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 182 - Number 5 |
| Year of Publication: 2018 |
| Authors: Neelendra Badal, Darpan Singh |
10.5120/ijca2018917538
|
Neelendra Badal, Darpan Singh . Analysis of Supervised Feature Selection Techniques on Animal Husbandry Dataset. International Journal of Computer Applications. 182, 5 ( Jul 2018), 18-24. DOI=10.5120/ijca2018917538
Abstract
Data mining techniques have become an obvious need of today’s high-dimensional animal industry data. In the last decade almost every aspect of animal related activities are being captured and stored either in local or central data repositories. Due to complex animal traits such as efficiency, growth, health, stress, behavior and adaptation, data mining is an area of challenge which can be optimally performed only with reduced number of relevant features. In this paper, a comparative analysis of various feature selection techniques based on some performance measuring parameter is presented using animal husbandry dataset. This research work finds J48 classifier to perform better in comparison to other traditional classification approaches.
References
- Booker, L. B., Goldberg, D. E., Holland, J. H. (1989/09). "Classifier systems and genetic algorithms." Artificial Intelligence 40(1-3): 235-282.
- Dash, M and H. Liu, 2000. “Feature selection for classification”, Intelligent Data Analysis., 1(1): 131-156.
- Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3 (Mar. 2003), 1289-1305.
- I.H. Witten, E. Frank and M.A. Hall, Data mining practical machine learning tools and techniques, Morgan Kaufmann publisher, Burlington 2011
- Jiménez, F., Gómez-Skarmeta, A.F., Sánchez, G., Deb, K.: An evolutionary algorithm for constrained multi-objective optimization. In: Proceedings IEEE World Congress on Evolutionary Computation (2002)
- John, G.H., Kohavi, R. and Pfleger, K., Irrelevant features and the subset selection problem. In: Proceedings of the Eleventh International Conference on Machine Learning, 121–129, 1994.
- Kohavi R. (1995) The power of decision tables. In: Lavrac N., Wrobel S. (eds) Machine Learning: ECML-95. ECML 1995.
- L. Zhang, F.M. Zhang, Y.F. Hu, “A Two-phase Flight Data Feature Selection Method Using both Filter and Wrapper” Proceedings of the Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007, SNPD 2007, pp.447-452.
- M. Hall and G. Holmes, "Benchmarking Attribute Selection Techniques for Discrete Class Data Mining," IEEE Transactions on Knowledge and Data Engineering, vol. 15, pp. 1437 - 1447, 2003.
- M.Dash and H.Liu, Feature selection for Classification, In Intelligent Data Analysis, vol. 1, no. 3,1997
- Tapas Ranjan Baitharu, Subhendu Kumar Pani, Analysis of Data Mining Techniques For Healthcare Decision Support System Using Liver Disorder Dataset, Procedia Computer Science 85 (2016) 862-870
- Vafai H., and De Jong, K. 1992, Genetic algorithms as a tool for feature selection in machine learning. In Fourth International Conference on Tools with Artificial Intelligence, 200-203, IEEE Computer Society Press
- Vasantha, M., Bharathy, V.S. 2010. "Evaluation of Attribute Selection Methods with Tree based Supervised Classification", International Journal of Computer Applications, Vol. 8, No. 12, pp. 35-38, (Oct. 2010)..
- Y.liu and M. Schumaan, Data mining feature selection for credit scoring models , Journal of Operation Research Society (2005) 56, pp. 1099-1108, published online 20 April 2005.
- Y. Saeys, I. Inza, and P. Larrañaga, “A review of feature selection techniques in bioinformatics,” Bioinformatics, 23(19), 2007, pp. 2507-2517.
- WEKA: Waikato Environment for Knowledge Analysis, http://www.cs.waikato.ac.nz/ml/weka.
Index Terms
Keywords