The week's pick
Reseach Article
An Improved Feature Selection and Classification using Decision Tree for Crop Datasets
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 142 - Number 13 |
| Year of Publication: 2016 |
| Authors: Surabhi Chouhan, Divakar Singh, Anju Singh |
10.5120/ijca2016909966
|
Surabhi Chouhan, Divakar Singh, Anju Singh . An Improved Feature Selection and Classification using Decision Tree for Crop Datasets. International Journal of Computer Applications. 142, 13 ( May 2016), 5-8. DOI=10.5120/ijca2016909966
Abstract
In this paper a more improved Feature Selection and Classification technique is implanted on Benchmark Datasets such as Mushroom and Soyabean. The Proposed Methodology implemented is based on the Hybrid Combinatorial method of Applying PSO-SVM for the selection of Features from the Dataset and Then Classification is done using Fuzzy Based Decision Tree. Experimental results when performed on Various Datasets prove that the proposed methodology extracts more features as well as provides more accuracy as compared to existing methodologies.
References
- Sudarshan Reddy S, Vedantha S, Venkateshwar Rao B, Sundar Ram Reddy and Venkat Reddy. Gathering Agrarian Crisis Farmers Suicides in Warangal district. Citizens Report, 1998.
- Anderson, W. K. (2010). Closing the gap between actual and potential yield of rainfed wheat. The impacts of environment, management and cultivar. . Field Crops Research, 116(1), 14-22.
- Asseng, S., & Pannell, D. J. (Adapting dryland agriculture to climate change: Farming implications and research and development needs in Western Australia. Climatic Change, 1-15, 2012.
- Drew, J. Operating In a Change Environment – NEAR/Drought Reform. Retrieved from (2010).
- J. Yang and V. Honavar, “Feature subset selection using a genetic algorithm,” IEEE Intelligent Systems and Their Applications, vol. 13, no. 3, pp. 44-49, March/April 1998.
- D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Reading Menlo Park: Addison-Wesley, vol. 412, 1989.
- Geraldin B. Dela Cruz, Bobby D. Gerardo, and Bartolome T. Tanguilig, “Agricultural Crops Classification Models Based on PCA-GA Implementation in Data Mining” International Journal of Modeling and Optimization, Vol. 4, No. 5, October 2014.
- A. Raoranne and R. V. Kulkarni, “Data Mining: An effective tool for estimation in the agricultural sector,” International Journal of Emerging Trends and Technology in Computer Science, vol. 1, no. 2, pp. 75-79, July-August 2012.
- D. Gerardo, J. Lee, I. Ra, and S. Byun, “Association rule discovery in data mining by implementing principal component analysis,” Artificial Intelligence and Simulation, Springer, Berlin Heidelberg, 2005, pp. 50-60.
- Farina, R., Seddaiu, G., Orsini, R., Steglich, E., Roggero, P.P., Francaviglia, R., (2011). Soil carbon dynamics and crop productivity as influenced by climate change in a rainfed cereal system under contrasting tillage using EPIC. Soil Till. Res. 112, 36–46.
- Dr. D. Ashok Kumar, N. Kannathasan, “A Survey on Data Mining and Pattern Recognition Techniques for Soil Data Mining” IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 3, No. 1, May 2011.
- S.Veenadhari, Dr. Bharat Misra, Dr. CD Singh, “Data mining Techniques for Predicting Crop Productivity –A review article”, International Journal of Computer Science and technology, march 2011.
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