A Novel Cluster of Quarter Feature Selection Based on Symmetrical Uncertainty

Abstract

Due to the diversity of sources, a large amount of data is being produced and also has various

problems including mislabeled data, missing values, imbalanced class labels, noise and high

dimensionality. In this research article, we proposed a novel framework to address high

dimensionality issue with feature reduction 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 , divide 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 recording improved accuracy than the

already available feature selection methods in the literature. In this research, we used filter-based

feature selection methods such as GRAE, IG, CHI-SQUARE (CHI 2), Relief to compare the

quarter of features produced by proposed technique.

Keywords

• Data Mining,Feature Selection,Filter,Pre-Processing,Symmetric Uncertainty

References

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