An efficient and holistic approach to reduce output and dependent parameters for multi–output Learning

  • Benny Thomas et. al

Abstract

 Multiple outputs are predicted simultaneously in multi output learning when an input is provided. Multi output learning is very important in making decisions involving real world complex systems. Multiple output analysis can be viewed as repetitions of single output parameter analysis. However, single output parameter analysis doesn’t provide comprehensive view of the complex systems as the interaction between all output parameters are lost. Such a complex system can also be found in an academic environment through the intervention of holistic education. Through holistic education, these output parameters can be bettered which in turn would better the human being as a whole. More holistic insight into the complex systems can be obtained by multi omics analysis. The aim is to predict simultaneously the multiple outputs to solve complex decision making problems.  This paper proposes a model for identification of dominating input and output parameters for multi omics. Results are shown to prove the concept using various available datasets.

Published
2020-02-02
How to Cite
et. al, B. T. (2020). An efficient and holistic approach to reduce output and dependent parameters for multi–output Learning. International Journal of Advanced Science and Technology, 29(04), 25 - 33. Retrieved from http://sersc.org/journals/index.php/IJAST/article/view/4032