• Vadivu.T
• B.Sumathi

Feature selection is essential for large datasets to eliminate irrelevant and redundant data. Various optimization techniques is used in Software defined networking. This is commonly used method in machine learning. Backward Elimination Iterative Distance Correlation (BE-IDC) method is proposed to solve the fixed threshold values for clustering based feature selection. It is one of the unsupervised cluster based feature selection method for adaptive threshold determination. BE-IDC automatically and dynamically generates threshold values which improves the accuracy of the system.

SDN, Feature Selection, Backward Elimination Iterative Distance Correlation.

"Feature Selection using Backward Elimination Iterative Distance Correlation method for adaptive threshold determination ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.366-369, May-2019, Available :http://www.jetir.org/papers/JETIR1905D52.pdf

"Feature Selection using Backward Elimination Iterative Distance Correlation method for adaptive threshold determination ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp366-369, May-2019, Available at : http://www.jetir.org/papers/JETIR1905D52.pdf