A Novel Cluster of Quarter Feature Selection Based on Symmetrical Uncertainty

Due to the diversity of sources, a large amount of data is being produced. The captured data associated with several problems including mislabeled data, missing values, imbalanced class labels, noise and high dimensionality. In this research article, we proposed a novel framework to address the high dimensionality issue with feature selection to increase the classification performance of various lazy learners, rule-based induction, Bayes, and tree-based models. In this research, we proposed robust Quarter Feature Selection (QFS) framework based on Symmetrical Uncertainty Attribute Evaluator. Our proposed technique analyzed with Six real world datasets. The proposed framework, divides the whole data space into 4 sets (Quarters) of features without duplication. Each such quarter has less than or equals 25 % features of whole data space. Practical results recorded that, one of the quarter, sometimes more than one quarter recorded improved accuracy than the traditional feature selection methods in the literature. In this research, we used filter-based feature selection methods such as Gain Ratio (GRAE), Information Gain (IG), Chi Squared (CHI 2), Relief to compare the quarter of features created by proposed technique.