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Algorithm for Producing Compact Decision Trees for Enhancing Classification Accuracy in Fertilizer Recommendation of Soil

by Navneet, Nasib Singh Gill
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 98 - Number 2
Year of Publication: 2014
Authors: Navneet, Nasib Singh Gill
10.5120/17153-7204

Navneet, Nasib Singh Gill . Algorithm for Producing Compact Decision Trees for Enhancing Classification Accuracy in Fertilizer Recommendation of Soil. International Journal of Computer Applications. 98, 2 ( July 2014), 8-14. DOI=10.5120/17153-7204

@article{ 10.5120/17153-7204,
author = { Navneet, Nasib Singh Gill },
title = { Algorithm for Producing Compact Decision Trees for Enhancing Classification Accuracy in Fertilizer Recommendation of Soil },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 98 },
number = { 2 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 8-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume98/number2/17153-7204/ },
doi = { 10.5120/17153-7204 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:25:08.760874+05:30
%A Navneet
%A Nasib Singh Gill
%T Algorithm for Producing Compact Decision Trees for Enhancing Classification Accuracy in Fertilizer Recommendation of Soil
%J International Journal of Computer Applications
%@ 0975-8887
%V 98
%N 2
%P 8-14
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining is the process of automatic classification of cases, based on data patterns obtained from a data set. Number of algorithms has been developed and implemented to extract information and discover knowledge patterns that may be useful for decision support. This paper proposes a technique that compact the decision tree increase the classification accuracy. The algorithm is developed by cascading the clustering and decision tree classification algorithm. The algorithm completes the process of two steps. In the first step, clustering is performed on training instances and in second step then the classification occurs on the clusters. A Schwartz criterion is used to get the optimal number of clusters. The algorithm is tested with the soil data set and various other online available datasets using WEKA. The simulation result shows that compact tree is formed, and the classification accuracy of the proposed algorithm is better than the classification accuracy of existing algorithms. The paper also presents the real-world application of proposed work in recommendation of fertilizers for soil dataset.

References
  1. Kruse ,R. , Riccia ,G. , Della et. al. ,2000,Computational Intelligence in Data Mining, Springer, New York, NY, USA.
  2. Stonebraker, M. , Agrawal ,R. et. al. ,1993,. DBMS Research At A Crossroads: The Vienna Update. In Proc. of the 19th VLDB Conference, pp 688-692, Dublin, Ireland.
  3. Chen M. S. , Han J. , and Yu P. S. ,December 1996. Data mining: An overview from database perspective. IEEE Transactions on Knowledge and Data Engg. , 8(6): pp. 866-883.
  4. Mac Queen, J. B. ,1967, Some Methods For Classification And Analysis Of Multivariate Observations. Proc. 5th Berkeley Symp. Mathematical Statistics and Probability, University of California Press, pp 281- 297.
  5. Hastie, T. and Tibshirani, R. ,1996, Discriminant Adaptive Nearest Neighbor Classification, IEEE Transaction on Pattern Analysis and Machine Intelligence, 18(6),pp 607-616.
  6. Tan, Pang-Ning, Steinbach, Michael and Kumar Vipin,2006,. Introduction to Data Mining, Addison Wesley.
  7. Mitchell, T. M. ,1997,. Machine Learning, McGraw-Hill Companies, USA.
  8. Singh, Nanhay,2012, Data Mining With Regression Technique, Journal of Information Systems and Communication ISSN: 0976-8742 & E-ISSN: 0976-8750, Volume 3, Issue 1,pp. 199-202.
  9. Methods Manual-Soil Testing in India,2011, Department of Agriculture & Cooperation Ministry of Agriculture Government of India.
  10. Gholap, Jay, et al, 2012, Soil Data Analysis Using Classification Techniques and Soil Attribute Prediction. arXiv preprint arXiv:1206. 1557.
  11. Wu, X. , Kumar ,V. et. al. ,2008, Top 10 algorithms in data mining, Knowledge Information System.
  12. Gaddam, R. , Shekhar et. al . ,March 2007, K-Means+ID3: A Novel Method for Supervised Anomaly Detection by Cascading K-Means Clustering and ID3 Decision Tree Learning Methods, IEEE transactions on knowledge and data engineering, vol. 19, no. 3.
  13. Muniyandi, Amuthan, Prabakar et. al. ,2012, Network Anomaly Detection by Cascading K-Means, International Conference on Communication Technology and System Design 2011,Elsevier , Procedia Engineering 30 – pp174 – 182.
  14. Cavanaugh, Joseph E. , and Neath. Andrew. ,1999. A. Generalizing the derivation of the Schwarz information criterion. Communications in Statistics-Theory and Methods 28. 1: 49-66.
  15. Quinlan, J. R. ,1993, C4. 5: Programs for Machine Learning, Morgan Kaufmann.
  16. Natural Resources Conservation Service, United States Department of Agriculture. Website:" http://soils. usda. gov/survey/geography/ssurgo/description_statsgo2. html"
Index Terms

Computer Science
Information Sciences

Keywords

Data mining c4. 5 WEKA k-mean clustering Schwarz criteria

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