@article{MAKHILLIJSC20149521219,
    title = {Improving Performanc of Supervised Learners Using Unsupervised Variable Selection Algorithm: A Novel Approach},
    journal = {International Journal of Soft Computing},
    volume = {9},
    number = {5},
    pages = {303-307},
    year = {2014},
    issn = {1816-9503},
    doi = {ijscomp.2014.303.307},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2014.303.307},
    author = {D. Asir Antony Gnana,S. Appavu Alias and},
    keywords = {Variable selection,reducing dimensionality,supervised learning,unsupervised learning,ranking,EM clustering,predictive model},
    abstract = {Prediction influences the technological advancement in various 
  sectors includes in finance for predicting the behavior of the stock market, 
  in sports for predicting the outcome of the event, in opinion polls for predicting 
  the outcome of the election and in many applications predicting their related 
  unseen or unknown data. The prediction can be performed by the supervised learners 
  also known as classifiers and their performance mainly relay on the variables 
  taken to learning for building the predictive model hence, the variable should 
  be relevant to the target concept of the leering process and these variables 
  should not be redundant. Identifying the relevancy and redundancy of variables 
  is called as variable selection. This is a preprocessing stage of knowledge 
  discovery in prediction. Most of the variable selection processes are performed 
  by some statistical or mathematical measures. This study presents a novel way 
  of selecting the variables form the training dataset using unsupervised learners 
  for enhancing the performance of the supervised learners in terms of increasing 
  accuracy and reduce time taken to build the predictive model. The performance 
  of this algorithm is evaluated with fourteen dataset with predictors namely 
  Naive Bayes (NB), Instance Based (IB1) and tree based J48.}
    }