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.
Digital Object Identifier (DOI)
Senthilkumar, D.; K. Reshmy, A.; and G. Kavitha, M.
"Non-Linear Machine Learning Techniques for Multi-Label Image Data Classification,"
Applied Mathematics & Information Sciences: Vol. 12:
6, Article 8.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol12/iss6/8