TY  - JOUR
T1  - Improvising Classification Performance for High Dimensional and
Small Sample Data Sets
AU - Kamatchi Priya, L. AU - Kavitha Devi, M.K. AU - Nagarajan, S. 
JO  - Asian Journal of Information Technology
VL  - 17
IS  - 4
SP  - 261
EP  - 270
PY  - 2018
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2018.261.270
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2018.261.270
KW  - Feature subset selection
KW  -filter method
KW  -correlation-based spanning tree
KW  -supervised classification
KW  -datasets
KW  -proposed framework
AB  - Classification is an important problem where the performance of a classifier depreciates as the sample
size decrease and dimensionality increase. This study describes feature subset selection framework for
supervised classification problem which works efficiently with very few training samples. In the proposed
algorithm, the most relevant feature has been selected by using filter method and the redundancy among the
features is eliminated by using correlation-based spanning tree. The proposed framework is designed to perform
data analytics to extract the most influencing predictors. The complexity of the algorithm is reduced drastically
by performing parallel processing of feature subsets. The performance of the algorithm is tested against various
predominant feature subset selection algorithms in 4 different datasets from UCI repository and 2 real world
microarray data where the classification accuracy of the proposed framework is better than the others feature
selection algorithms.
ER  -