Sumant, Archana Shivdas
Ensemble feature subset selection : integration of symmetric uncertainty and chi-square techniques with RreliefF - Vol.103(3), June - New York Springer 2022 - 831-844p.
The emanation of the high-dimensional data processing induces severe problems and challenges besides the apparent benefits. High-dimensional data analysis demands a huge requirement for processing. In this paper, we have proposed multistage methods ChS-R (Chi-square integrated with RReliefF) and SU-R (Symmetric Uncertainty integrated with RReliefF) for ranking features. The proposed integrated feature ranking methods use different statistical methods to select appropriate feature subset. The methods are integrated to overcome issues of one method with benefits of other method. The Chi-square (ChS) test is initially applied to select top n features, followed by RReliefF. In RReliefF algorithm, attributes are selected according to their suitability for the target function. It gives global view of attribute quality for further dimensionality reduction. In addition RReliefF deals with noisy, incomplete and multi-class data. Similarly, Symmetric Uncertainty (SU) integrated with RReliefF approach is proposed. The results are validated with random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) classifiers. The proposed systems are compared with SU, ChS, Relief and Ensemble Feature Selection with Mutual Information (EFS-MI) methods. The proposed approach achieves 89.48% dimensionality reduction.
Humanities and Applied Sciences
Ensemble feature subset selection : integration of symmetric uncertainty and chi-square techniques with RreliefF - Vol.103(3), June - New York Springer 2022 - 831-844p.
The emanation of the high-dimensional data processing induces severe problems and challenges besides the apparent benefits. High-dimensional data analysis demands a huge requirement for processing. In this paper, we have proposed multistage methods ChS-R (Chi-square integrated with RReliefF) and SU-R (Symmetric Uncertainty integrated with RReliefF) for ranking features. The proposed integrated feature ranking methods use different statistical methods to select appropriate feature subset. The methods are integrated to overcome issues of one method with benefits of other method. The Chi-square (ChS) test is initially applied to select top n features, followed by RReliefF. In RReliefF algorithm, attributes are selected according to their suitability for the target function. It gives global view of attribute quality for further dimensionality reduction. In addition RReliefF deals with noisy, incomplete and multi-class data. Similarly, Symmetric Uncertainty (SU) integrated with RReliefF approach is proposed. The results are validated with random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) classifiers. The proposed systems are compared with SU, ChS, Relief and Ensemble Feature Selection with Mutual Information (EFS-MI) methods. The proposed approach achieves 89.48% dimensionality reduction.
Humanities and Applied Sciences