Class for building and using a simple decision table majority classifier.
For more information see:
Ron Kohavi: The Power of Decision Tables. In: 8th European Conference on Machine Learning, 174-189, 1995.
BibTeX:
@inproceedings{Kohavi1995, author = {Ron Kohavi}, booktitle = {8th European Conference on Machine Learning}, pages = {174-189}, publisher = {Springer}, title = {The Power of Decision Tables}, year = {1995} } Valid options are:
-S <search method specification> Full class name of search method, followed by its options. eg: "weka.attributeSelection.BestFirst -D 1" (default weka.attributeSelection.BestFirst)
-X <number of folds> Use cross validation to evaluate features. Use number of folds = 1 for leave one out CV. (Default = leave one out CV)
-E <acc | rmse | mae | auc> Performance evaluation measure to use for selecting attributes. (Default = accuracy for discrete class and rmse for numeric class)
-I Use nearest neighbour instead of global table majority.
-R Display decision table rules.
Options specific to search method weka.attributeSelection.BestFirst:
-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)
@version $Revision: 5981 $