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An Empirical Comparison of Supervised Learning Processes.
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International Journal of Engineering (IJE)
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Volume:  1    Issue:  1
Pages:  1-53
Publication Date:   June 2007
ISSN (Online): 1985-2312
Pages 
21 - 38
Author(s)  
Sanjeev Manchanda - India
Mayank Dave - India
S. B. Singh - India
 
Published Date   
30-06-2007 
Publisher 
CSC Journals, Kuala Lumpur, Malaysia
ADDITIONAL INFORMATION
Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
 
KEYWORDS:   Data Mining, Knowledge Discovery in Databases, Supervised learning algorithms, Stacking 
 
 
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Data mining as a formal discipline is only two decades old, but it has registered phenomenal development and has become a mature discipline in this short span. In this paper, we present an empirical study of supervised learning processes based on empirical evaluation of different classification algorithms. We have included most of the supervised learning processes based on different pre pruning and post pruning criteria. We have included ten datasets, collected from internationally renowned agencies. Different specific models are presented and results are generated. Issues related to different processes are analyzed suitably. We also present a comparison of our study with benchmark results of different datasets and classification algorithms. We have presented results of all algorithms with fifteen different performance measures out of a set of twenty three calculated measures, making it a comprehensive study. 
 
 
 
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1 Punjabi University, Patiala (India)
 
 
 
Sanjeev Manchanda : Colleagues
Mayank Dave : Colleagues
S. B. Singh : Colleagues  
 
 
 
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