Data-driven based Optimal Feature Selection Algorithm using Ensemble Techniques for Classification

The shift in paradigm with advanced Machine Learning algorithms will help to face the challenges such as computational power, training time, and algorithmic stability. individual feature selection techniques, hardly give appropriate subsets, that might be vulnerable variations induced at input data thus led wrong conclusions. An expedient technique should designed for approximating relevance improve performance data. Unlike prevailing novelty of proposed Data-driven based Optimal Feature Selection (DOFS) algorithm is optimal k-value ‘kf’ determined by effective minimizes complexity expands prediction power using gradient descent method. experimental analysis demonstarted ensemble techniques non-communicable disease diabetes mellitus dataset produces an accuracy 80.80%, whereas comparative benchmark depicts improved 86.03%.