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01-Applied Mathematics & Information Sciences
An International Journal
               
 
 
 
 
 
 
 
 
 
 
 

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Volumes > Volume 12 > No. 6

 
   

Non-Linear Machine Learning Techniques for Multi-Label Image Data Classification

PP: 1139-1145
doi:10.18576/amis/120608
Author(s)
D. Senthilkumar, A. K. Reshmy, M. G. Kavitha,
Abstract
In this paper, we propose non-linear Machine Learning Techniques (MLT) for Multi-label Image Classification (MLIC) problems. Multi-label Learning requires MLT to identify the complex non-linear relationship between the features and class labels. Also, Multi-label data degrades the performance of the classifiers and processing of this data with a large number of features is too complex while using traditional methods. Therefore, we propose two approaches namely ensemble Deep Learning Network (DLN) and Multivariate Adaptive Regression Splines (MARS) for MLIC. The experimental results show that the proposed (DLN and MARS) algorithms achieves a superior predictive performance rate of 94.77% and 81.68% respectively, compared to the existing methods.

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