TY  - JOUR
T1  - Towards Better Classification Using Improved Particle Swarm Optimization
Algorithm and Decision Tree for Dengue Datasets
AU - Renuka Devi, B. AU - Nageswara Rao, K. AU - Pallam Setty, S. 
JO  - International Journal of Soft Computing
VL  - 11
IS  - 1
SP  - 18
EP  - 25
PY  - 2016
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2016.18.25
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2016.18.25
KW  - Datasets
KW  -noisy
KW  -PSO
KW  -patients
KW  -time
AB  - This study presents a novel methodology based on Particle Swarm Optimization algorithm to model a new classification system. In the creation of classifier, feature selection frequently used to remove in
appropriate and noisy features asto retrieve relevant features. Physically developing a feature set can be very time taking and expensive attempt. PSO is an intelligent search methodology that employs a population of individuals prevailing within a multi-dimensional space. This study employs the correlation between the
attributes as the fitness function to Particle Swarm Optimization algorithm. The proposed approach is applied to Clinical Dengue Datasets that retrieve optimal features and the obtained results shows the accuracy and validity of the approach. The proposed methodology is analyzed on dengue data set that is downloaded from http://www.ncbi.nlm.nih.gov/gds. The data set contains 18 attributes of 1275 patients.
ER  -