explorer.props

代码是一个分类器的实现,其中使用了部分weka的源代码。可以将项目导入eclipse运行

# This props file contains default values for the Weka Explorer.
#
# Notes:
# - backslashes within options, e.g., for the default "Classifier", need
#   to be doubled (the backslashes get interpreted already when a property
#   is read).
#
# Author  FracPete (fracpete at waikato dot ac dot nz)
# Version $Revision: 1.3 $

# if set to true the Capabilities filters in the GOE will be initialized
# based on the full dataset that has been loaded into the Explorer 
# otherwise only the header (true|false)
# Note: The tabs in the Explorer have their own class combobox, which means 
#       that the data has to be inspected several times (changing the class
#       combobox only leads to an inspection of the data in the current tab), 
#       which can be slow on big datasets.
InitGenericObjectEditorFilter=True

# the default filter, including options (can be left empty)
Filter=

# the default classifier in the classify tab, including options
# (default is ZeroR)
Classifier=weka.classifiers.rules.ZeroR

# the default test mode in the classify tab 
# (according to "testMode" variable in startClassifier method)
# 1 - cross-validation
# 2 - percentage split
# 3 - use training set
# 4 - supplied test set
# (default is 1 - CV)
ClassifierTestMode=1

# the default number of folds for CV in the classify tab 
# (default is 10)
ClassifierCrossvalidationFolds=10

# the default percentage split % in the classify tab (integer: 1-99)
# (default is 66)
ClassifierPercentageSplit=66

# whether the classifier model is output (true|false)
# (default is true)
ClassifierOutputModel=true

# whether additional per-class stats of the classifier model are 
# output (true|false)
# (default is true)
ClassifierOutputPerClassStats=true

# whether the entropy based evaluation measures of the classifier model are 
# output (true|false)
# (default is false)
ClassifierOutputEntropyEvalMeasures=false

# whether the confusion matrix is output for the classifier (true|false)
# (default is true)
ClassifierOutputConfusionMatrix=true

# whether the predictions of the classifier are stored for visulization 
# purposes (true|false)
# (default is true)
ClassifierStorePredictionsForVis=true

# whether the predictions of the classifier output as well (true|false)
# (default is false)
ClassifierOutputPredictions=false

# whether the evaluation of the classifier is done cost-sensitively (true|false)
# (default is false)
# Note: a cost matrix still has to be provided!
ClassifierCostSensitiveEval=false

# the default random seed in the classify tab
# (default is 1)
ClassifierRandomSeed=1

# whether the order is preserved in case of percentage split in the classifier 
# tab 
# (default is false)
ClassifierPreserveOrder=false

# the default clusterer, including options
# (default is EM)
Clusterer=weka.clusterers.EM

# the default test mode in the cluster tab 
# (according to "testMode" variable in startClusterer method)
# 2 - percentage split
# 3 - use training set
# 4 - supplied test set
# 5 - classes to clusters evaluation
# (default is 3 - training set)
ClustererTestMode=3

# whether the clusters are stored for visualization purposes (true|false)
# (default is true)
ClustererStoreClustersForVis=true

# the default associator, including options
# (default is Apriori)
Associator=weka.associations.Apriori

# the default attribute evaluator, including options
# (default is CfsSubsetEval)
ASEvaluation=weka.attributeSelection.CfsSubsetEval

# the default attribute selection search scheme, including options
# (default is BestFirst)
ASSearch=weka.attributeSelection.BestFirst

# the default test mode in the attribute selection tab 
# (according to "testMode" variable in startAttributeSelection method)
# 0 - use full training set
# 1 - cross-validation
# (default is 0 - full training set)
ASTestMode=0

# the default number of folds for CV in the attribute selection tab 
# (default is 10)
ASCrossvalidationFolds=10

# the default random seed in the attribute selection tab
# (default is 1)
ASRandomSeed=1