Creates a panel that displays the attributes contained in a set of
instances, letting the user toggle whether each attribute is selected
or not (eg: so that unselected attributes can be removed before
classification).
This panel allows the user to select and configure an attribute
evaluator and a search method, set the
attribute of the current dataset to be used as the class, and perform
attribute selection using one of two selection modes (select using all the
training data or perform a n-fold cross validation---on each trial
selecting features using n-1 folds of the data).
This panel allows the user to select and configure a classifier, set the
attribute of the current dataset to be used as the class, and evaluate
the classifier using a number of testing modes (test on the training data,
train/test on a percentage split, n-fold cross-validation, test on a
separate split).
This panel allows the user to select and configure a clusterer, and evaluate
the clusterer using a number of testing modes (test on the training data,
train/test on a percentage split, test on a
separate split).
Outputs a new integer vector which contains all the values in two integer vectors; assuming list1 and list2 are
incrementally sorted and no identical integers within each integer vector
DatabaseResultProducer examines a database and extracts out
the results produced by the specified ResultProducer
and submits them to the specified ResultListener.
An abstract class for instance filters: objects that take instances
as input, carry out some transformation on the instance and then
output the instance.
This instance filter takes a range of N numeric attributes and replaces
them with N-1 numeric attributes, the values of which are the difference
between consecutive attribute values from the original instance.
A PropertyEditor for objects that themselves have been defined as
editable in the GenericObjectEditor configuration file, which lists
possible values that can be selected from, and themselves configured.
Gets a description of the internal settings of the result
producer, sufficient for distinguishing a ResultProducer
instance from another with different settings (ignoring
those settings set through this interface).
Gets a description of the internal settings of the result
producer, sufficient for distinguishing a ResultProducer
instance from another with different settings (ignoring
those settings set through this interface).
Gets a description of the internal settings of the result
producer, sufficient for distinguishing a ResultProducer
instance from another with different settings (ignoring
those settings set through this interface).
Gets a description of the internal settings of the result
producer, sufficient for distinguishing a ResultProducer
instance from another with different settings (ignoring
those settings set through this interface).
Gets a description of the internal settings of the result
producer, sufficient for distinguishing a ResultProducer
instance from another with different settings (ignoring
those settings set through this interface).
Supposedly returns an initialization string to create a classifier
identical to the current one, including it's state, but this doesn't
appear possible given that the initialization string isn't supposed to
contain multiple statements.
Supposedly returns an initialization string to create a Object
identical to the current one, including it's state, but this doesn't
appear possible given that the initialization string isn't supposed to
contain multiple statements.
Supposedly returns an initialization string to create a classifier
identical to the current one, including it's state, but this doesn't
appear possible given that the initialization string isn't supposed to
contain multiple statements.
Abstract attribute subset evaluator capable of evaluating subsets with
respect to a data set that is distinct from that used to initialize/
train the subset evaluator.
Conditional probability estimator for a numeric domain conditional upon
a discrete domain (utilises separate kernel estimators for each discrete
conditioning value).
K* is an instance-based classifier, that is the class of a test
instance is based upon the class of those training instances
similar to it, as determined by some similarity function.
KStar() -
Constructor for class weka.classifiers.kstar.KStar
A custom class which provides the environment for computing the
transformation probability of a specified test instance nominal
attribute to a specified train instance nominal attribute.
A custom class which provides the environment for computing the
transformation probability of a specified test instance numeric
attribute to a specified train instance numeric attribute.
Creates a cost matrix for the class attribute of the supplied instances,
where the misclassification costs are higher for misclassifying a rare
class as a frequent one.