Open Access
ARTICLE
Effective and Efficient Ranking and Re-Ranking Feature Selector for Healthcare Analytics
S.Ilangovan1,*, A. Vincent Antony Kumar2
1 K. L. N. College of Engineering, Madurai, India.
2 PSNA College of Engineering and Technology, Dindigul, India
* Corresponding Author: S. Ilangovan,
Intelligent Automation & Soft Computing 2020, 26(2), 261-268. https://doi.org/10.31209/2019.100000154
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.
Keywords
Cite This Article
Ilangovan, S., Vincent, A. (2020). Effective and Efficient Ranking and Re-Ranking Feature Selector for Healthcare Analytics.
Intelligent Automation & Soft Computing, 26(2), 261–268.