@Article{2019.100000154,
AUTHOR = {S.Ilangovan, A. Vincent Antony Kumar},
TITLE = {Effective and Efficient Ranking and Re-Ranking Feature Selector for  Healthcare Analytics},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {2},
PAGES = {261--268},
URL = {http://www.techscience.com/iasc/v26n2/39946},
ISSN = {2326-005X},
ABSTRACT = {In this work, a Novel Feature selection framework called SU embedded PSO 
Feature Selector has been proposed (SU-PSO) towards the selection of optimal 
feature subset for the improvement of detection performance of classifiers. The 
feature space ranking is done through the Symmetrical Uncertainty method. 
Further, memetic operators of PSO include features and remove features are 
used to choose relevant features and the best of best features are selected 
using PSO. The proposed feature selector efficiently removes not only irrelevant 
but also redundant features. Performance metric such as classification accuracy, 
subset of features selected and running time are used for comparison.},
DOI = {10.31209/2019.100000154}
}