@article{MAKHILLRJAS2014949374,
    title = {Double Linear Support Vector Machine for Dimensionality Reduction},
    journal = {Research Journal of Applied Sciences},
    volume = {9},
    number = {4},
    pages = {208-213},
    year = {2014},
    issn = {1815-932x},
    doi = {rjasci.2014.208.213},
    url = {https://makhillpublications.co/view-article.php?issn=1815-932x&doi=rjasci.2014.208.213},
    author = {Wipawan and},
    keywords = {Data dimensions,dimensionality reduction,DLSVM,feature selection,Linear SVM Weight},
    abstract = {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.}
    }