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In this report, we studied different complex classification models such as Gradient Descent, Multiclass Classification. We also used different Machine Learning tools such as LibSVM, MEGAm, and FastDT to design a complex classifier based on OVA and AVA approaches. We also designed 2 different Rank classifier using MEGAm library and evaluated its performance on the OHSUMED database. The binary classification accuracy (0-1) error using 20 different queries and 10 retrieved documents for each query was 33% for Ranking Classifier 1. The binary classification accuracy for Ranking Classifier 2 was 37%. However, the average ranking performance, as evaluated using DCG metric, was roughly 8% better for Ranking Classifier 2 as compared with Ranking Classifier 1. This improvement comes from the cost function used to penalize the mis-ranking.

 
 
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Keywords

Machine  Learning,  Complex  classifiers  
 
 
 About This Document
 
 Subject Computer Science
 Category Term Paper
 Views 1380
 Downloads 206
 Added 19-01-12
 Contributor   ravigarg
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