Classification of soybean pest data using decision tree algorithm


Abstract views: 33 / PDF downloads: 1

CLASSIFICATION OF SOYBEAN PEST DATA USING DECISION TREE ALGORITHM

Authors

  • V JINUBALA Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore-641 021, Tamil Nadu
  • R LAWRANCE Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore-641 021, Tamil Nadu

https://doi.org/10.56739/jor.v33i3.137984

Keywords:

C4.5 classification, Data mining, Decision tree classification, Soybean pest data

Abstract

Classification of large volume of data especially in agriculture is a challenging task. Decision tree method is generally used for the classification, because it is the simple hierarchical structure for the user understanding and decision making. In the present study, the various classification techniques have been applied with Spodoptera spp. solitary larvae data set ofsoybean, for classifying into four classes based on Economic Threshold Level (ETL), using R statistical language. Out of six classification methods tested, it was found that C4.5 (decision tree) was effective with accuracy of 78 per cent followed by Naïve Bayes and kNN algorithms both with 72 per cent accuracy.

Downloads

Download data is not yet available.

References

Alagukumar S and Lawrance R 2015. A selective analysis of microarray data using association rule mining. Procedia Computer Science, 47: 3-12.

Alagukumar S and Lawrance R 2015 Algorithm for microarray cancer data analysis using frequent pattern mining and gene intervals. International Journal of Computer Applications, 1 : 9-14.

Han J and Kamber M 2005. Data Mining: Concepts and Techniques. Elsevier Publications, pp. 744.

Hssina B, Merbouha A, Ezzikouri H and Erritali M 2014. A comparative study of decision tree ID3 andC4. 5. International Journal of AdvancedComputer Science and Applications, 4(2): 13-19.

Srinivasan P and Aggarwal C C 2003. On the use of conceptual reconstruction forminingmassively incomplete data sets. IEEE Transactions on Knowledge and Data Engineering, 15: 1512-1521.

Priyam A, Abhijeeta R G, Ratheeb A and Srivastava B S 2013. Comparative analysis of decision tree classification algorithms. International Journal of Current Engineering and Technology, 3: 334-337.

Quinlan J R 1996. Improved use of continuous attributes in C4.5. Journal of Artificial Intelligence Research, 4: 77-90.

Quinlan J R2014. C4.5: Programs for Machine Learning. Elsevier Publications, pp. 302.

Safavian S R and Landgrebe D 1991. A survey of decision tree classifier methodology. IEEE Transactions on Systems. Man and Cybernetics, 21: 660-674.

Downloads

Submitted

2023-06-19

Published

2016-10-21

How to Cite

V JINUBALA, & R LAWRANCE. (2016). Classification of soybean pest data using decision tree algorithm: CLASSIFICATION OF SOYBEAN PEST DATA USING DECISION TREE ALGORITHM. Journal of Oilseeds Research, 33(3). https://doi.org/10.56739/jor.v33i3.137984