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

by Navneet, Nasib Singh Gill
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.

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Index Terms

Computer Science
Information Sciences

Keywords

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