Examples of weka.attributeSelection.BestFirst

weka.attributeSelection.BestFirst

BestFirst:

Searches the space of attribute subsets by greedy hillclimbing augmented with a backtracking facility. Setting the number of consecutive non-improving nodes allowed controls the level of backtracking done. Best first may start with the empty set of attributes and search forward, or start with the full set of attributes and search backward, or start at any point and search in both directions (by considering all possible single attribute additions and deletions at a given point).

Valid options are:

 -P <start set> Specify a starting set of attributes. Eg. 1,3,5-7.

 -D <0 = backward | 1 = forward | 2 = bi-directional> Direction of search. (default = 1).

 -N <num> Number of non-improving nodes to consider before terminating search.

 -S <num> Size of lookup cache for evaluated subsets. Expressed as a multiple of the number of attributes in the data set. (default = 1)

@author Mark Hall (mhall@cs.waikato.ac.nz)Martin Guetlein (cashing merit of expanded nodes) @version $Revision: 1.29 $

   */
  protected void resetOptions() {


    m_trainSelector = new weka.attributeSelection.AttributeSelection();
    setEvaluator(new CfsSubsetEval());
    setSearch(new BestFirst());
    m_SelectedAttributes = null;
    m_FilterOptions = null;
  }

   */
  protected void resetOptions() {


    m_trainSelector = new weka.attributeSelection.AttributeSelection();
    setEvaluator(new CfsSubsetEval());
    setSearch(new BestFirst());
    m_SelectedAttributes = null;
    m_FilterOptions = null;
  }

   */
  protected void resetOptions() {


    m_trainSelector = new weka.attributeSelection.AttributeSelection();
    setEvaluator(new CfsSubsetEval());
    setSearch(new BestFirst());
    m_SelectedAttributes = null;
    m_FilterOptions = null;
  }

Examples of weka.attributeSelection.BestFirst

Related Classes of weka.attributeSelection.BestFirst