TY  - JOUR
T1  - Double Linear Support Vector Machine for Dimensionality Reduction
AU - Buathong, Wipawan AU - Meesad, Phayung 
JO  - Research Journal of Applied Sciences
VL  - 9
IS  - 4
SP  - 208
EP  - 213
PY  - 2014
DA  - 2001/08/19
SN  - 1815-932x
DO  - rjasci.2014.208.213
UR  - https://makhillpublications.co/view-article.php?doi=rjasci.2014.208.213
KW  - Data dimensions
KW  -dimensionality reduction
KW  -DLSVM
KW  -feature selection
KW  -Linear SVM Weight
AB  - This study proposes an alternative feature selection technique 
  for dimensionality reduction namely Double Linear Support Vector Machine or 
  “DLSVM” Weight. The efficiency 
  of DLSVM was measured based on four performance evaluation criteria (i.e., accuracy, 
  F-measure, precision and recall). The efficiency of well recognised feature 
  selection techniques was also measured for comparative purposes. The Support 
  Vector Machine (SVM), a prominent classifier was also used with DLSVM and these 
  feature selection techniques. The Leukemia dataset from the University of California 
  Irvine (UCI) machine learning repository was used for the experiments. Downsized 
  data dimensions were classified into 60, 50, 40, 30, 20 and 10, respectively. 
  The experimental results showed that the DLSVM was much more efficient than 
  other feature selection techniques at almost all of the data dimensions. Particularly, 
  all performance evaluation criteria of DLSVM could reach 100% when original 
  data dimensions were downsized from 5,147-60, 50 and 40.
ER  -